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  • DAVID MALAN: So today we're going to talk

  • about challenges at this crucial intersection of law and technology.

  • And the goal at the end of today is not to have provided you with more answers,

  • but hopefully generated more questions about what this intersection is

  • and where we're going to go forward.

  • Because at this intersection lie a lot of really interesting and challenging

  • problems that are at the forefront of what we're doing.

  • And you, as a practitioner, may be someone

  • who is asked to confront and contend with and provide resolutions

  • for some of these problems.

  • This lecture's going to be divided into two parts roughly.

  • In the first part, we're going to discuss

  • trust, whether we can trust the software that we receive

  • and what implications that might have for software

  • that's transmitted over the internet.

  • And in the second part, we're going to talk about regulatory challenges that

  • might be faced.

  • As new emergent technologies come into play,

  • how is the law prepared, or is the law prepared

  • to contend with those challenges?

  • But let's start by talking about this idea of a trust model,

  • trust model being a computational term for basically

  • do we trust something that we're receiving over the internet?

  • Do we trust that software is what it says it is?

  • Do we trust that a provider is providing a service in the way they describe,

  • or are there doing other things behind the scenes?

  • Now, as part of this lecture, there's a lot of supplementary reading materials

  • that we've incorporated in that we're going to draw on quite a bit

  • throughout the course of today.

  • And the first of those is a paper called "Reflections on Trusting Trust."

  • This is arguably one of the most famous papers in computer science.

  • It was written in 1984 by Ken Thompson.

  • Ken Thompson was one of the inventors of the Unix operating

  • system, on which Linux was based, on which subsequently,

  • based on a version of Linux, Mac OS is based.

  • And so he's quite a well-known figure in the computer science community.

  • And he wrote this paper to accept an award called the Turing Award, again,

  • one of the most famous awards in computer science.

  • And in it, he's trying to highlight the problem of trust in software.

  • And he begins by discussing about a computer

  • program that can reproduce itself.

  • We typically refer to this as what's called a quine in computer science.

  • But the idea is can you write a simple program that reproduces itself?

  • And we won't go through that exercise here.

  • But Thompson shows us that, yes, it is relatively trivial actually

  • to write programs that do this.

  • But what does this then lead to?

  • So the next step of the process that Thompson discusses is

  • stage two in this paper, is how do you teach a computer

  • to teach itself something?

  • And he uses the idea of a compiler.

  • Recall that we use compilers in some programming languages

  • to turn source code, the human-like syntax

  • that we understand-- languages like C, for example,

  • will be written in source code.

  • And they need to be compiled, or transformed,

  • into zeros and ones, machine code, because computers only

  • understand these zeros and ones.

  • They don't understand the human-like syntax

  • that we're familiar with as programmers when we are writing our code.

  • And what Thompson is suggesting that we can do

  • is we can teach the compiler, the program that

  • actually takes the source code and transforms it into zeros and ones,

  • to compile itself.

  • And he starts out by doing this by introducing a new character

  • for the compiler to understand.

  • The analogy is drawn to the newline character, which we

  • type when we reach the end of a line.

  • We want to go down and back to the beginning of a new one.

  • We enter the newline character.

  • There are other characters that were not initially envisioned

  • as part of the C compiler.

  • And one of those is vertical tab, which basically

  • allows you to jump down several lines without necessarily resetting back

  • to the beginning of the line as newline would.

  • And so Thompson goes through the process,

  • that I won't expound on here because it's

  • covered in the paper, of how to teach the compiler what

  • this new character, this vertical tab means.

  • He shows us that we can write code in the C programming language

  • and then have the compiler compile that code into zeros and ones that

  • create something called a binary, a program

  • that a computer can execute and understand.

  • And then we can use that newly created compiler

  • that we've just created to compile other C programs.

  • Which means that once we've taught the computer how

  • to understand what this vertical tab character is,

  • it then can propagate into any other C program that we write.

  • The computer is learning, effectively, a new thing to interpret,

  • and it can then interpret that in every other program.

  • But then Thompson leads us into stage three,

  • which is, what if that's not all the computer or the compiler does?

  • What if instead of just adding that vertical tab character

  • whenever we did it, we also secretly, as part of the source code,

  • insert a bug into the code, such that now whenever we compile the code

  • and we encounter that backslash V, that vertical tab character,

  • we're not only putting that into the code

  • so that the computer can understand and pass this slash

  • V, the character that it never knew about before,

  • but we've also sort of surreptitiously hidden a bug in the code.

  • And again, Thompson goes into great detail

  • about exactly how that can be done and exactly what steps we can then

  • take to make it look like that was never there.

  • We can change the source code, modify it,

  • and make it look like we never had a bug in there,

  • even though it is now propagating into all of the source code

  • we ever write or we ever compile going forward.

  • We've created a way to surreptitiously hide bugs in our code.

  • And the conclusion that Thompson draws is, is it

  • possible to ever trust software that was written by anyone else?

  • In this course we've talked about some of the tools that are available

  • to programmers that would allow them to go back in time-- for example,

  • we've discussed GitHub on several occasions to go back in time--

  • and see prior versions of code.

  • In the 1980s, when this paper was written,

  • that wasn't necessarily possible.

  • It was relatively easy to hide source code changes so that the untrained eye

  • wouldn't know about them.

  • Code was not shared via the internet.

  • Code was shared via floppy disks or hard disk that were being

  • passed between people who needed them.

  • And so there was no easy way to verify that code that was written by somebody

  • else is actually trustworthy.

  • Now, again, this paper came out 35-plus years ago now.

  • And it came out around the time that the Computer Fraud and Abuse

  • Act, which we've also previously discussed,

  • was being drafted and run through Congress.

  • Did lawmakers heed the advice of Ken Thompson?

  • Do we still today trust that our programs that we receive

  • or that we write are free of bugs?

  • Is there a way for us to verify that?

  • What should happen if code is found to be buggy?

  • What if it's unintentionally buggy?

  • What if it's maliciously buggy?

  • Do we have a way to challenge things like that?

  • Do we have a way to prosecute those kinds of cases

  • if the bug creates some sort of catastrophic failure in some business?

  • Not exactly.

  • The challenge of figuring out whether or not we should trust software

  • is something that we have to contend with every day.

  • And there's no bright line answer for exactly how to do so.

  • Now let's turn to perhaps a more modern interpretation of this idea

  • and take a look at the Samsung Smart TV policy.

  • So this was a bit of news a few years ago,

  • that Samsung was recording or was capturing voice commands

  • so people could make use of their television without needing a remote.

  • You could say something like, television,

  • please turn the volume up, or television, change the channel.

  • But it turned out that when Samsung was collecting this information,

  • they were transmitting it to a third party, a third-party language

  • processor, who would ostensibly be taking the commands they hear

  • and feeding them into their own database to improve the quality of understanding

  • what these commands were.

  • So it would hear--

  • let's say thousands of people use this brand of television.

  • It would take the thousands of people's voices all making the same command,

  • feed it into its algorithm to process this command, and hopefully try

  • and come up with a better or more comprehensive understanding of what

  • that command meant to avoid the mistake of I say one thing,

  • and the TV does something else because it misinterprets what I do.

  • If you take a look at Samsung's policy, it says things like the device

  • will collect IP addresses, cookies, your hardware and software configuration, so

  • the settings that you have put onto your television, your browser information.

  • Some of these TVs, these smart TVs, have web browsers built into them.

  • And so you may be also sharing information about your history

  • and so on.

  • Is this necessarily a bad thing?

  • When it became a news story it was mildly scandalous in the tech world

  • because it was unexpected.

  • No one thought that that was something a television should be doing.

  • But is it really all that different from when you use your browser anyway?

  • We've seen in this course that whenever we connect to a website,

  • we need to provide our IP address so that the site that we're requesting,

  • the server, knows where to send our data back to.

  • And in addition.

  • As part of those HTTP headers, we not only send our IP address,

  • but we're usually sending information about what operating system or running,

  • what browser we're currently using, where geographically we

  • might be located, so ways to help the routers route

  • traffic in the right direction.

  • Are we leaking as much information when we

  • use the internet to make a request as we are when our television is interpreting

  • or understanding a command?

  • Why is it that this particular action, this interpretation of sound,

  • feels so much more of a privacy violation

  • than just accessing something on the internet when we're voluntarily, sort

  • of, revealing the same information?

  • Are we not voluntarily relinquishing the same information

  • to a company like Samsung, whose smart TVs sort of precipitated this?

  • Moreover, is it technologically feasible for Samsung

  • to not collect all of the sounds that it hears?

  • One of the big concerns as well that came up

  • with these smart TVs is that when does the recording and transmitting start?

  • For those of you who maybe have seen old versions of Star Trek,

  • you may recall that in order to activate the computers on that television show,

  • someone would just say computer.

  • And then the computer would sort of spring to life,

  • and then they could have a normal English language interaction with it.

  • There's no need to program specific commands

  • or click anything or have any other interaction other than voice.

  • How would we technologically accomplish that now?

  • How would a device know whether or not it

  • should be listening unless it's listening for a specific word?

  • Is there a way for the device to perhaps listen

  • to everything that comes in but only start sending information

  • when it hears a command?

  • Is it impossible for it not to capture all of the information

  • that it's hearing and send it somewhere, encrypt it or not encrypt it, and just

  • transmit it somewhere else?

  • It's kind of an interesting question.

  • Samsung also allows not only voice controls, but gesture controls.

  • This may help people who are visually impaired

  • or help people who are unable to use a remote control device.

  • They can wave or make certain gestures.

  • And in so doing, they're going to capture your face perhaps

  • as part of this gesture.

  • Or they may capture certain movements that you're making

  • or maybe even capture, depending on the quality

  • of the camera built into the television, aspects of the room around you.

  • Is this necessarily problematic?

  • Is this something that we as users of this software

  • need to accept as something that just is part of the deal?

  • In order to use this feature, we have to do it?

  • Is there a necessary compromise?

  • Is there a way to ensure that Samsung is properly interacting with our data?

  • Should there be a way for us to verify this?

  • Or is that proprietary to Samsung, the way that it handles that data?

  • Again, these are all sorts of questions that we really

  • want to know the answers to.

  • We want to know whether or not what we are saying we're doing is secure,

  • is private.

  • And we can read the policies of these organizations that are providing

  • these tools for us to interact with.

  • But is that enough?

  • Do we have a way to verify?

  • Is there anything we can do other than just trust

  • that these companies are doing what they say they're doing,

  • or services or programmers are providing tools that

  • do exactly what they say that they do?

  • Without some really advanced knowledge and skill in tech, the answer is no.

  • And even if you have that advanced skill or knowledge,

  • it's really hard to take a look at a binary, zeros

  • and ones, the actual executable program that is being run on these devices,

  • and look at it and say, yeah, I think that that does match the source code

  • that they provided to me so I can really feel

  • reasonably confident that yeah I trust this particular piece of software.

  • As we've discussed in the context of security,

  • trust is sort of something we have to deal with.

  • We're constantly torn between this tension of not trusting other people,

  • and so we encrypt everything, but needing to trust people in order

  • for some things to work.

  • It's a very delicate balancing act that we have to contend with every day.

  • And again, I don't mean to pick on Samsung here.

  • This is just one of many different examples

  • that have sort of existed in popular culture.

  • Let's consider another one, for example.

  • Let's consider a piece of hardware called the Intel Management

  • Engine, or hardware, firmware, software, depending

  • on what it is, because one of the open questions

  • is, what exactly is the Intel Management Engine?

  • What we do know about it is that it is usually part of the CPU itself.

  • It's unclear.

  • It's not exactly been publicly disclosed whether it's built into the CPU

  • or perhaps built into the CMOS or the BIOS, different parts, low-level parts

  • of the motherboard itself.

  • But it is a chip or some software that runs on a computer, whose intended

  • purpose is to help network administrators in the event

  • that something has gone wrong with a computer.

  • So recall that we previously discussed this idea

  • that it's possible to encrypt your hard drive,

  • and that there are also ramifications that

  • can happen if you encrypt your hard drive

  • and forget exactly how to un-encrypt your hard drive.

  • What the Intel Management Engine would allow, one of its several features,

  • is for a network administrator, perhaps if you're

  • in an enterprise suite, your IT professional, your head of IT

  • might be able to access your computer remotely by issuing commands,

  • because the computer is able to listen on a specific port.

  • It's like 16,000 something.

  • I don't remember exactly the port number.

  • And it's discussed again, as well, in the article provided.

  • But it allows the computer to be listening

  • for a specific kind of request that should only

  • be coming from an administrator's computer to be able to remotely access

  • another computer.

  • But the concern is because it's listening on a specific port,

  • how is it possible to ensure that the request that it's

  • receiving on that port or via that IP address are accurate?

  • Because Intel has not disclosed the actual code

  • that comprises this module of the IME.

  • And then the question becomes, is that a problem?

  • Should they be required to reveal that code?

  • Some will certainly argue yes it's really important for us

  • as end users to understand what software is running on our devices.

  • We have a right to know what programs are running on our computers.

  • Others will say, no, we don't have a right to do that.

  • This is Intel's intellectual property.

  • It may contain trade secret information that allows its chips to work better.

  • We don't, for example, argue Coca-Cola should

  • be required to reveal its secret formula to us because it may implicate

  • certain allergies or Kentucky Fried Chicken needs

  • to disclose its secret recipe to us.

  • So why should Intel be required to tell us

  • about the lines of code that comprise this part of its hardware or software

  • or firmware, again depending on exactly what it is, because it's slightly

  • unclear as to what this tool is.

  • So the question again is, are they required

  • to provide some degree of transparency?

  • Do we have a right to know?

  • Should we just trust that this software is indeed

  • only being used to allow remote access only to authorized individuals?

  • If Intel were to provide a tool to tell us whether our computer was

  • vulnerable to attack from outside computers accessing

  • our own personal computers outside of the enterprise context,

  • should we trust the result of the software

  • that Intel provided that tells us whether or not it is vulnerable?

  • As it turns out, Intel does provide this software

  • to tell you whether or not your IME chip is activated in such a way

  • that yes, you are subject to potential remote access or no, you are not.

  • Does saying that you are or your aunt reveal potential trade

  • secret-related information about Intel?

  • Should we be concerned that Intel is the one providing us

  • this information versus a third party providing us this information?

  • Of course, Intel being the only organization

  • that really can tell us that we're vulnerable

  • or not because they're the only ones who know what is on this software.

  • So again, not picking on any individual company

  • here, just drawing from case studies that exist in popular culture

  • from in tech circles about the kinds of questions

  • that we need to start considering and wrestling with.

  • Are they going to be required to disclose this information?

  • Should Samsung be revealing information about what sorts of data

  • it's collecting and how it's collecting it?

  • Do we trust that our compilers, as Ken Thompson alluded to,

  • actually compile our code the way that they say that they do?

  • This healthy skepticism is always at the forefront of our mind

  • when we're considering programming- and technology-related questions.

  • But how do we press on these issues further in a legal context?

  • That's still to be determined.

  • And that's going to be something that we're

  • going to be grappling with for quite some time, I think.

  • Another key issue that's likely to be faced by technologists

  • and the lawyers who represent them, particularly

  • startups working in a small environment with limited numbers of programmers

  • that may be relying on material that's been open sourced online,

  • is this idea of open source software and licensing.

  • Because the scheme that exists out there is quite complicated.

  • There are many, many different licenses that

  • have many, many different provisions associated with them.

  • And each one will have different combinations

  • of some of these things being permitted, some of them not,

  • and potential ramifications of using some of these licenses.

  • We're going to discuss three of the most popularly used licenses, particularly

  • in the context of open source software, generally that is released on GitHub.

  • And the first of these is GPL version 3, GPL being the new Public License.

  • And one of the things that GPL often gets criticism for

  • is it is known as a copyleft license.

  • And copyleft is sort of designed to be the inverse of what copyright

  • protection's usually thought of.

  • Copyright protections give the owner or the person who owns the copyright, not

  • necessarily the creator but the person who owns the copyright, the ability

  • to restrict certain behaviors associated with that work or that material.

  • The GPL sort of does the opposite.

  • Instead of restricting the rights of others,

  • it compels others, who use code that has been licensed under the GPL,

  • to avoid allowing any restrictions at all,

  • such that others can also benefit from using and modifying

  • that same source code.

  • The catch with GPL is that any code that incorporates the GPL--

  • GPL license, excuse me.

  • Any code that includes GPL-licensed code--

  • so say you incorporate some module written by somebody else,

  • or your client incorporate something that they found on GitHub

  • or found on the internet and wants to include it into their own project.

  • If that code is licensed under the GPL, unfortunately one of the side effects

  • perhaps of what your client or what you have just done

  • is you have transformed your entire work into something that

  • is GPL, which means you are also then required to make the source

  • code available to anybody, make the binary available to anybody,

  • and also to allow anybody to have the same rights of modification

  • and redistribution that you had as well.

  • So think about some of the dangers that might introduce for a company that

  • relies extensively on GPL license code.

  • They may not be able to profit as much from that code

  • as they thought they would.

  • Perhaps they thought they had this amazing disruptive idea that

  • was going to transform the market.

  • And this particular piece of GPL code that they found online

  • allowed them-- it was the final piece of the puzzle that they needed.

  • When they included it in their own source code,

  • they transformed their entire project, according

  • to the terms of the GPL license, into something that was also GPL licensed.

  • So their profitability-- they could still sell it.

  • But their profitability may be diminished because the source code is

  • available freely to anybody to access.

  • Now, some people find this particularly restrictive.

  • In fact, pejoratively sometimes this is referred

  • to as the GNU virus, the General Public License virus,

  • because it propagates so extensively.

  • As soon as you touch code or use code really

  • that is GPL licensed, suddenly everything

  • that it touches is also GPL licensed.

  • So it's, depending on your perspective of open source licensing,

  • it's either a great thing because it's making more stuff available,

  • or it's a bad thing because it is preventing people

  • from using open source material to create further developments when they

  • don't necessarily want to license those changes or modifications that they

  • made.

  • The lesser General Public License, or the lesser GNU Public License,

  • is basically the same idea, but it only applies to a library code.

  • So if code is LGPL-ed, what this basically means

  • is any modifications that you make to that code also need to be LGPL-ed,

  • or released under the LGPL license.

  • But other ancillary things that you do in your program that

  • overall incorporates this library code does not need to be LGPL-ed.

  • So it would be possible to license it under other terms,

  • including terms that are not open source at all.

  • So changes that you make to the library need

  • to be propagated down the line so that other people can

  • benefit from the changes that are specific to the library that you made.

  • But it does not necessarily reflect back into your own code.

  • You don't have to necessarily make that publicly available.

  • So this is considered slightly lesser in terms of its ability to propagate.

  • And also, though, it's considered lesser in terms of its ability

  • to grant rights to others.

  • Then you have, at the other end of the extreme, the MIT license.

  • The MIT license is considered one of the most permissive licenses available.

  • It says, here's the software.

  • Do whatever you want with it.

  • You can make changes to it.

  • You don't have to re-license those changes to others.

  • You can take this code and profit from it.

  • You can take this code and make whatever-- re-license it

  • under some other scheme if you want.

  • So this is the other end of the extreme.

  • Is this license copyleft?

  • Well, no, it's not copyleft because it doesn't require others

  • to adhere to the same licensing terms.

  • Again, you can do with it whatever you would like.

  • Most of the code that is actually found on GitHub is MIT licensed.

  • So in that sense, using code that you find online

  • is not necessarily problematic to an entrepreneur or a budding developer who

  • wants to profit from some larger program that they write if it incorporates

  • MIT-licensed code, which might be an issue for those who are incorporating

  • GPL-licensed code.

  • What sorts of considerations, then, would

  • go into deciding which license to use?

  • And again, these are just three of many, many licenses that exist

  • that pertain to software development.

  • Then, of course, there are open source licenses

  • that are not tied to this at all.

  • So for example, a lot of the material that we produce for CS50,

  • the course on which this is based at Harvard College,

  • is licensed under a Creative Commons license,

  • which is similar in spirit to a GPL license,

  • in as much as it oftentimes will require people to re-license the changes that

  • they make to that material under GPL--

  • or under Creative Commons, excuse me.

  • It will generally require a non-commercial aspect of it.

  • It is not possible to profit from any changes that you make and so on.

  • And that's not a software license.

  • That's more of a general media-related license.

  • So these software open source licenses exist in both contexts.

  • But what sorts of considerations might go into choosing a license?

  • Well, again, it really does depend on the organization itself.

  • And so that's why understanding a bit about these licenses

  • certainly comes into play.

  • Do you want your changes to propagate and get out into the market

  • more easily?

  • That might be a reason to use the MIT license, which is a very permissive.

  • Do you just feel compelled to share code with others,

  • and you want to insist that others share that code as well?

  • Then you might want to use GPL.

  • Do you potentially want to use open source code

  • but not release your own code freely to others, the changes

  • that you make to interact with that code?

  • That might be cause for relying on LGPL for the library code

  • that you import and use but licensing your own changes and modifications

  • under some other scheme.

  • Again, a very complex and open field that's

  • going to require a lot of research for anyone who's

  • going to be pursuing and helping clients who

  • are working with software development and what they want

  • to do with that code going forward.

  • So let's turn our attention now from issues that have existed for a while

  • and sort of been bubbling underneath the surface,

  • issues of trust and issues of software licensing--

  • those have been around a lot longer--

  • and start to contend with new technologies

  • and how the law keeps up with them.

  • And so you'll also hear these terms that are

  • being considered emergent technologies or new technologies.

  • You'll sometimes see them referred to as disruptive technologies

  • because they are poised to materially affect the way that we interact

  • with technology, particularly in terms of purchasing things

  • through commerce, for example, as in the case of our first topic, 3D printing.

  • So how does 3D printing work, is a good question to ask at the outset.

  • Similar in spirit to a 2D printer, with a 2D printer

  • you have a write head that spits out ink, typically in some sort of toner.

  • It moves left to right across a piece of paper.

  • And the paper's also fed through some sort of feeder.

  • So the left-to-right movement of the toner or ink head

  • is the x-axis movement.

  • And the paper rolling underneath that provides y-axis movements.

  • Such that when we're done, we may be able to get access

  • to a piece of paper that has ink scattered across it, left to right,

  • top to bottom.

  • 3D printers work in very much the same way, except instead of their medium,

  • instead of being ink or toner, is typically some sort of filament that

  • is conventionally, at least at the time of this recording, been

  • generally plastic based.

  • And what basically happens is the plastic

  • is melted just to above the melting point of the plastic.

  • And then it is deposited onto some surface.

  • And that surface that is being moved over by a similar read-write head,

  • basically it's a nozzle or eyedropper basically of plastic.

  • And it can move up and down across a flat surface,

  • similar to what the printer would do.

  • But instead of just being flat, the arm can also move up and down.

  • On some models of 3D printers, the table can move up and down

  • to allow it to not only print on the xy-plane, but also on the z-axis.

  • So it can print in space and create three-dimensional objects, 3D printing.

  • Typically the material used, again, is melted plastic just

  • above the melting point.

  • So that by the time it's deposited onto the surface

  • or onto other existing plastic, it's already basically cooled

  • enough that it's hardened again.

  • So the idea is we want to just melt it enough so

  • that by the time it's put onto some other surface,

  • it re-hardens and becomes a rigid material once again.

  • Now, 3D printing is usually considered to be a disruptive technology

  • because it allows people to create items they may not otherwise have access to.

  • And of course, the controversial one that

  • is often spoken about in terms of we need to ban things

  • or we need to ban certain 3D printers or ban certain 3D printing technologies

  • is guns, because it's actually possible, using technology

  • that exists right now, to 3D print a plastic gun that

  • would evade any sort of metal detection that is usually used for detecting guns

  • and is fully functional.

  • It can fire bullets, plastic bullets or real metal bullets.

  • The article that is recommended that goes with this part of the discussion

  • proposes several different ways that we might be able to--

  • or the law may be able to keep up with 3D printing technologies.

  • Because, again, the law typically lags behind technology, and so

  • is there a way that the law can contend with this?

  • And there are a couple of options that it proposes

  • that I think are worthy of discussion.

  • The first is allow permission-less innovation.

  • Should we just allow people to do whatever

  • they want with it, the 3D printing technology,

  • and decide ex post facto this, what you just did, is not OK,

  • the rest of it's fine and disallow that type of thing going forward?

  • This approach is interesting because it allows people to be creative,

  • and it allows potentially for things to be

  • revealed about 3D printing technology that were not

  • possible to forecast in advance.

  • But is that reactive-based approach better?

  • Or should we be proactive in trying to prevent

  • the production of certain things that we don't want to be produced?

  • And moreover, all the plastic filament tends

  • to be the most popular and common way that things are 3D printed right now.

  • 3D printers are being developed that are much more advanced than this.

  • We are not necessarily restricted to plastic-based printing.

  • We may have metal-based printing.

  • And you may have even seen that there are 3D printers that exist

  • that can produce organic materials.

  • They use human cells, basically, to create things like organs.

  • Do we want people to be able to create these things?

  • Is this the kind of thing that should be regulated beforehand rather

  • than regulated after we've already printed

  • and exchanged copyrighted designs for what to build and construct?

  • Is it too late by the time we have regulated it to prevent it

  • from being reproduced in the future?

  • Another thought that this article proposes is immunizing intermediaries.

  • Should we allow people to do whatever they want with 3D printing?

  • Or maybe not allow people to do whatever they want 3D printing,

  • but regardless don't punish the manufacturers of 3D printers

  • and don't punish the designers of the CAD files,

  • the Computer-Aided Design files, that generally go into 3D printing?

  • Is this a reasonable policy approach?

  • It's not an unheard of policy approach.

  • This is the approach that we typically have used with respect

  • to gun manufacturers, for example.

  • Gun manufacturers generally are not subject to prosecution for crimes

  • that are committed using those guns.

  • Should we apply something similar to 3D printers, for example,

  • when the printer is used to manufacturer a gun?

  • Who should be punished in that case, the person who

  • designed the gun model, the person who actually

  • printed the gun, the 3D printer manufacturer itself,

  • any of those people?

  • Again, an unanswered question that the law is going

  • to have to contend with going forward.

  • Another solution potentially is to rely on existing common law.

  • But the problem that typically arises there

  • is that there is not a federal common law.

  • And so this would potentially result in 50 different jurisdictions handling

  • the same problem in different ways.

  • Whether this is a good thing or a bad thing, again,

  • sort of dependent on how quickly these things move.

  • Common law, as we've seen, certainly is capable of adapting

  • to new technologies.

  • Does it do it quickly enough for us?

  • Finally, another example that is proposed

  • is that we could just allow the 3D printing industry to self-regulate.

  • After all, we, as attorneys, self-regulate,

  • and that seems to work just fine.

  • Now, granted this may be because we are in an adversarial system,

  • and so there's advantages and extra incentives for adversaries

  • to insist that we are adhering to our ethical principles

  • and doing the right thing.

  • There's also the overhanging threat of outside regulation

  • if we do not self-regulate.

  • So in a lawyer context, adapting this model to 3D printing

  • may work because it seems to be working well for attorneys.

  • Then you consider that social media companies are also

  • self-regulating, with respect to data protection and data privacy.

  • And as we've seen, that's maybe not going so well.

  • So how do we handle the regulation of 3D printing?

  • Does it fall into the self-regulation category?

  • Does that succeed?

  • Does it fall into the self-regulation category that doesn't succeed?

  • Does it require preemptive regulation to deal with?

  • Now, 3D printing also has some other potential concerns.

  • Very easily, by the nature of the technology itself,

  • it's quite capable of violating copyrights, patents, trademarks,

  • potentially more just by the virtue of the fact

  • that you can create things that may be copywritten or patented or trademarked.

  • And there's also prior case law that sort of informs potential consequences

  • for using 3D printers, the Napster case from several years ago, the technology.

  • Napster would allow peer-to-peer sharing of digital music files.

  • Basically that service was deemed to entirely exist

  • for the purpose of violating copyright.

  • And so that shut down Napster basically.

  • Will 3D printers suffer the same fate?

  • Because you could argue that 3D printers are generally used to recreate things

  • that may be patented or may be subject to copyright.

  • Or is it going to fall more into a category like Sony, which

  • many years ago faced a lawsuit, or was part of a lawsuit involving VCRs

  • and tape-delaying copywritten material?

  • Is that going to be more of a precedent for 3D printing,

  • or is the Napster case going to be more of a precedent for 3D printing?

  • Again, we don't really know.

  • It's up to the future practitioners of technology law, who

  • are forced to grapple with the challenges presented by 3D printing,

  • to nudge us in that direction, one way or the other.

  • To dive a bit more deeply into this topic of 3D printing,

  • I do recommend you take a look at this article, "Guns Limbs and Toys--

  • What Future for 3D Printing?"

  • And if you're particularly interested in 3D printing and some

  • of the ramifications of it and the technological underpinnings of it,

  • I do encourage you to also take a look at "The Law and 3D Printing," which

  • is a Law Review article from 2015, which also is periodically updated online.

  • And it's a wonderful bibliography of all the different things

  • that 3D printing does.

  • And it will presumably continue to be updated as cases and laws come

  • into play that interact with 3D printing and start to define this relatively

  • ambiguous space.

  • Another particularly innovative space that

  • really pushes the boundaries of what the law is capable of handling

  • is the idea of augmented reality and virtual reality.

  • And we'll consider them in that order.

  • Let's define what augmented reality is.

  • And the most common example of this that you may be familiar with

  • is a phenomenon from several years ago called Pokemon Go.

  • It was a game that you played on your mobile phone.

  • And you would hold up your phone, and you

  • would see through the camera's lens, as if you

  • were taking a picture, the real world through the lens of the camera.

  • But superimposed onto that would be digital avatars

  • of Pokemon, which is part of this game of collectible creatures

  • that you're trying to walk around and find and capture, basically.

  • So you would try and throw some fake ball at them to capture them.

  • So augmented reality is some sort of technical graphical overlay

  • over the real world.

  • Contrast this with virtual reality, in which one typically

  • wears a headset of some sort.

  • It's usually proprietary.

  • It's not generally available as an app, for example,

  • like the augmented-reality game Pokemon Go was.

  • It's usually tied to a specific brand of headset,

  • like Oculus being one type of headset, for example.

  • And it is an immersive alternate reality basically.

  • When you put the headset on, you don't see the lens of the world around you.

  • You are transformed into another space.

  • And to make the experience even more immersive

  • is the potential to wear headphones, for example,

  • so that you are not only immersed in a visual space,

  • but also immersed in a soundscape.

  • Now, something that's particularly strange about these environments

  • is that they are still interactive.

  • It is still possible for multiple people, scattered

  • in different parts of the world, to be involved in the same virtual reality

  • experience, or the same augmented-reality experience.

  • Let's now consider virtual reality experiences, where

  • you are taken away from the real world.

  • What should happen if someone were to commit a crime in a virtual reality

  • space?

  • Studies have shown that people who are immersed in a virtual reality

  • experience can have serious ramifications.

  • They can have real feelings that last for a long time

  • based on their experiences in them.

  • For example, there's been a study out where people put on a virtual reality

  • headset, and they were then immersed in this space where

  • they were standing on a plank.

  • And they were asked to step off the plank.

  • Now, in the real world, this would be just like this room.

  • I can see that everything around me is a carpet.

  • There's no giant pit for me to fall into.

  • But when I have this headset on, I'm completely taken away from reality

  • as we see it here.

  • The experience is so pervasive for some people

  • that they walk to the edge of the plank, and they freeze in fear.

  • They can't move.

  • There's a real physical manifestation in the real world

  • of what they feel in this reality.

  • And for those brave people who are able to take the step off the edge,

  • many of them lean forward and try and fall into the space.

  • And some of them may even get the experience

  • like when you're on a roller coaster, and you feel that tingle in your spine

  • as you're falling.

  • The sense that that actually is happening to you

  • is so real in the virtual reality space that you can feel it.

  • So what would be the case, then, if you are in a virtual reality space,

  • and someone were to pull a virtual gun on you?

  • Is that assault?

  • Assault is a crime where your perception of harm is a material element.

  • It's not actual harm.

  • It's your perception of it.

  • You can perceive in the real world when somebody points a gun at you,

  • this fear of imminent bodily harm.

  • Can you feel that same imminent bodily harm in a virtual world?

  • That's not a question that's really been answered Moreover,

  • who has jurisdiction over a crime that is committed in virtual reality?

  • It's possible that I, here in the United States,

  • might be interacting with someone in France,

  • who is maybe the perpetrator of this virtual assault that I'm describing.

  • Is the crime committed in the United States?

  • Is the crime committed in France?

  • Do we have jurisdiction over the potential perpetrator,

  • even though all I'm experiencing or seeing

  • is that person's avatar as opposed to their real persona?

  • Does anyone have jurisdiction over it?

  • Does the jurisdiction only exist in the virtual world?

  • Virtual reality introduces a lot of really interesting questions

  • that are poised to redefine the way we think about jurisdiction

  • in defining crimes and the prosecutability of crimes

  • in a virtual space.

  • Some other terms just to bring up as well that sort of tangentially

  • relate to virtual and augmented reality so that you're

  • familiar with them are the real-world crimes that are very technologically

  • driven of doxing and swatting.

  • Doxing, if unfamiliar, is a crime involving

  • revealing or exposing the personal information of someone

  • on the internet with the intent to harass or embarrass or do

  • some harm to them by having that exposed, so, for example,

  • revealing somebody's phone number such that it can

  • be called incessantly by other people.

  • As well as swatting, which is a, well, pretty horrible crime, whereby

  • an individual calls the police and says, John Smith

  • is committing a crime at this address, is holding me

  • hostage, or something like that, with the intention

  • that the police would then go to that location

  • and a SWAT team would go, hence the term swatting,

  • and potentially cause serious injury or harm to the ostensibly innocent John

  • Smith, who's just sitting at home doing nothing.

  • These two crimes are generally interrelated.

  • But they oftentimes come up in the technological context,

  • usually as part of the same conversation, when we're

  • thinking about virtual reality crimes.

  • One of the potential upsides, though, if you

  • want to think about it like that, of crimes that are committed

  • in virtual or augmented reality are--

  • well, there's actually a few.

  • First, because it is happening in a virtual space,

  • and because generally in the virtual space all of our movements are tracked,

  • and the identities of everybody who's entering and leaving

  • that space are tracked by way of IP addresses,

  • it may be easier for investigators to figure out who

  • the perpetrators of those crimes are.

  • You know exactly the IP address of the person who apparently initiated

  • this threat against you in the virtual space, which may perhaps make it easier

  • to go and find that person in reality and question them

  • about their involvement in this alleged crime.

  • The other thing that's fortunately a good thing about these crimes,

  • and this is not to mitigate the effect that these crimes can have,

  • is that usually you can kind of mute them from happening.

  • If somebody is in a virtual space, and they're just screaming constantly,

  • such that you might consider that to be disturbing the peace when you're

  • in a virtual space trying to have some sort of pleasant experience ordinarily,

  • you usually have the capability of muting them.

  • This is not a benefit that we have in real life.

  • We generally can't stop crimes by just pretending they're not happening.

  • But in a virtual space, we do have that luxury.

  • That's, again, not to mitigate some of the very unpleasant and unfortunate

  • things that can happen in virtual reality that are just inappropriate.

  • But being in that space does allow people

  • the option to get away from the crime in a way that the confines of reality

  • may not allow.

  • But again, this is a very challenging area

  • because the law is not really equipped right now

  • to handle what happens in an alternate reality, which effectively

  • virtual reality is.

  • And so, again, if you're considering trying to figure out the best

  • way to prosecute these issues or deal with these issues,

  • you may be at the forefront of trying to define how crimes

  • are dealt with in a virtual space.

  • Or how potentially, if working with augmented reality,

  • if malicious code is put up in front of you

  • to simulate something that might be happening in the real world,

  • how do you prosecute those kinds of crimes, where you may be, for example,

  • using a GPS program that is designed to navigate you

  • in one direction versus the other based on the set of glasses

  • that you're wearing so you don't have to keep looking at your phone to make sure

  • that you're going the right way.

  • What if somebody maliciously programs that augmented-reality program to route

  • you off a cliff somewhere, right?

  • How do we deal with that?

  • Right now, again, augmented-reality virtual reality,

  • it's a relatively untested space for lawyers in the law.

  • In the second part of today's lecture, we're

  • going to take a look at some potential regulatory challenges going forward,

  • some issues at the forefront of law and technology generally related to privacy

  • and how the law is ill equipped or hopefully

  • soon to be equipped to handle the challenge that these issues present.

  • And the first of these is your digital privacy,

  • in particular, the abilities of organizations, companies,

  • and mobile device manufacturers to track your whereabouts, whether that's

  • your digital whereabouts, where you go on the internet,

  • or your physical whereabouts.

  • We'll start with the former, your digital whereabouts.

  • So there's an article we provided on digital tracking technologies.

  • This is designed to be a primer for the different types of things

  • that companies, in particular their marketing teams,

  • may do to track individuals online with, again,

  • relatively little recourse for the individuals

  • to know what sorts of information is being gathered

  • about them, at least in the US.

  • Now, of course, we're familiar with this idea

  • of a cookie from our discussion of interacting with websites.

  • It's our shorthand way to bypass the logging credentials

  • and show sort of a virtual hand stamp saying, yes, I am who I say I am.

  • I've already previously logged into your service.

  • Cookies are certainly one way that a site

  • can track a recurrent user from coming to the site over and over and over.

  • Now, this article posits that most consumers have just

  • come to accept that they're being tracked,

  • like that's just part of the deal with the internet.

  • Do you think that using cookies and being tracked

  • is an essential requirement of what it means to use the internet today?

  • And if you do think that, is that the way it should be?

  • And if you don't think that, is that also the way it should be?

  • Or should we be considering the fact that tracking is happening?

  • Is that an essential part of what it means to use the internet?

  • We also need to be concerned about the types of data

  • that companies are using or collecting about us.

  • Certainly cookies are one way to identify who we are.

  • But also it's possible for a cookie to be identified with what types of data

  • an individual accesses while visiting a particular site.

  • So for example, if I am on Facebook, and I'm using my cookie,

  • and I'm looking up lots of pictures on Facebook--

  • I'm just I'm searching for all my friends

  • profiles and clicking on all the ones that have cats in them--

  • that might then give Facebook, or the administrator of that site,

  • the ability to pair that cookie with a particular trend of things

  • that that cookie likes.

  • So in this case, it might want to then-- it knows, OK, maybe the person who

  • owns this cookie likes cats.

  • And as such, it may then start to serve up

  • advertisements related to cats to me.

  • And then when I log into a site, it's going

  • to get information about my IP address.

  • And if I use that cookie, it has now mapped my IP address to the fact

  • that I like cats.

  • And then it could sell the information about me, this particular IP address--

  • I guess it's not necessarily me because one IP address usually covers a house

  • but gets you pretty close--

  • maps this particular IP address to somebody who likes cats.

  • So they may sell that to some other service.

  • Now, it turns out that IP addresses are generally

  • allocated in geographic blocks, which means that, again, just by virtue

  • of the fact that I log into a particular site,

  • I'm able to access and access similar data when visiting that site.

  • They may not be able to geographically isolate down to--

  • again, depending on how populated the area you are currently living in

  • is, possibly narrow it down to a city block, that someone in this city block

  • really likes cats.

  • And then this company may be involved in targeted actual physical mail

  • advertising, snail mail advertising, where

  • some company that sells cat products, like a pet store or something,

  • might target that particular block with advertising, in the hopes that because

  • of this data that has been collected about this particular cookie, who then

  • logged in with a particular IP address, which

  • we've zeroed in to a particular geographic location--

  • it's kind of feeling a little unsettling, right?

  • Suddenly something that we do online is having a manifestation, again,

  • in the real world, where we're getting targeted advertising not just

  • on sites that we visit, but also in our mailbox at home.

  • It's a little bit discomfiting.

  • Should IP addresses be allocated in this way?

  • Is this the kind of thing that technologically can be changed?

  • The latter answer is yes, it is possible to allocate

  • IP addresses in a different way than we typically do.

  • Should we allocate IP addresses in a different way than we typically do?

  • Is the potential threat of receiving real-life advertisements

  • related to your online activities enough to justify that?

  • What would be enough to justify that kind of change?

  • Then, of course, there's the question of tracking not in the digital world,

  • but in the real world.

  • This is usually done through mobile phone tracking.

  • And so we provide an article from the Electronic Frontier Foundation.

  • And full disclosure, some of the articles we've presented here

  • do have a certain bias in them.

  • The Electronic Frontier Foundation is well-known as a rights advocacy

  • group for privacy.

  • And so they're going to naturally be disinclined to things that

  • involve tracking of data and so on.

  • So just bear that in mind, some additional context

  • when you're considering this article.

  • But it does contain a lot of factual information and not

  • necessarily just purely opinion about things that should be changed.

  • Although it does advocate for certain policy changes.

  • Now, why is it that tracking on a mobile device

  • is oftentimes perceived as much worse than tracking on a laptop or desktop?

  • Well, again, first of all, it's your mobile device

  • is generally with you at all times.

  • We've reached the point where our phones are generally carried in our pockets

  • and with us wherever we go, which means that it's very easy to use data

  • that's collected from mobile phone--

  • information that's given out by the mobile phone,

  • whether that's the cell phone towers or GPS data and so on,

  • to pinpoint that to us.

  • The other concern is that mobile phones are very, very quick

  • to become obsolete.

  • Oftentimes one or two versions of a new version

  • of a phone, whether it's a new Android phone release or software

  • release or a new iPhone or so on, the version that came out two years ago

  • is generally obsolete, which means it is no longer subject to firmware patches

  • provided by the manufacturer or the software

  • developers of the operating systems that are

  • run on those phones, which could also mean that they are much more

  • susceptible to people figuring out how to break into those phones

  • and use that tracking information against you.

  • So laptops and desktops generally don't move that much.

  • You may carry your laptop to and from but generally

  • to just a couple locations.

  • It's usually set at a desk somewhere in between.

  • Your desktop, of course, doesn't move at all.

  • So the tracking potential there is pretty minimal.

  • And also those devices tend to last quite a long time,

  • and the lifecycle support for service and keeping those operating systems

  • up to date is quite a bit longer versus the mobile phone,

  • where that window is much, much shorter.

  • Now, phones, contrary to most people's opinions of this,

  • phones do not actually track your information based on GPS data.

  • The way GPS works is your phone just fires off a signal,

  • and it gets a response back that is trying to triangulate

  • where exactly you are in space.

  • But there's no information about what device requested that data or so on.

  • And generally that data's not stored on the phone or in the GPS satellite

  • in any way.

  • It's just sort of ask-and-answer type inquiry.

  • The real threat vector for phone tracking, if this is the kind of thing

  • that you're concerned about, is actually through cell phone towers

  • because cell phone towers do track this information.

  • Different companies own different towers.

  • They would like to know who is using each tower,

  • whether or not this may involve also charging the--

  • say I'm using a Verizon phone, and I happen

  • to be connected to an AT&T tower.

  • AT&T may wish to know that this is mostly being used by Verizon customers.

  • And the only way they really know that is

  • by mapping the individual device to the phone number,

  • then checking that against Verizon's records.

  • And so they are collecting all this information

  • about every phone that connects their tower so they could potentially

  • bill Verizon for the portion of their customers

  • who were using their infrastructure.

  • So these towers do track information.

  • And towers also can be used to triangulate your location.

  • If I'm standing in the middle of an open field, for example,

  • and there's a tower over there and a tower maybe just beside me,

  • generally the signal that I'm sending-- my phone

  • is emitting a signal constantly.

  • If I'm emitting one signal in that direction,

  • and it's received by a tower fairly weakly, and if I'm emitting another--

  • my phone is, again, radially sort of emitting the signal.

  • If right next to me is another tower that's

  • picking it up very strongly, in space I can

  • use the information, sort of extrapolating from these two points,

  • I'm most likely here.

  • So even without having GPS turned on, just by trying to make a phone call

  • or use a 2G, 3G, 4G network, it's pretty easy

  • to figure out where you are in space.

  • And this is potentially a concern.

  • This concern comes up sometimes in the context

  • of are these companies who provide operating systems for phones

  • or firmware for phones, are they at the behest of government agencies, who

  • may request back doors into the devices so that they can then

  • spy on individuals?

  • And certainly this might be something that

  • comes up in a FISA court or the like, where

  • they're trying to get phone records.

  • And there's always this sort of unknown.

  • Is it happening to all of our devices all the time?

  • Is it is it happening right now the phone in my pocket?

  • Or is the sound being captured in such a way

  • that it can be transmitted just because?

  • Because there happens to be a backdoor in the operating

  • system or a backdoor in the firmware that

  • allows anybody to listen to it, even if they're not

  • supposed to be listening to it.

  • It's really hard to pretend to be somebody that you're not with a phone.

  • As you saw, it's pretty easy to pretend to be somebody

  • that you're not with a computer you can use a service like a VPN, which

  • pretends to be a different IP address.

  • You connect to the VPN.

  • And as long as you trust VPN, the VPN ostensibly protects your identity.

  • With mobile phones, every device has a unique ID.

  • And it's really hard to change that ID.

  • So one way around this is to use what are

  • called burner phones, devices that are used once, twice,

  • and then they're thrown away.

  • Now, this again comes down to how concerned are you about your privacy?

  • How concerned should you be about your privacy?

  • Are you concerned enough that you're willing to purchase these devices that

  • are one-time, two-time use devices, which you then

  • throw away and constantly do that?

  • And moreover, it's actually kind of interesting to know

  • that burner phones don't actually do--

  • they're not shown to do anything to protect one's identity or privacy

  • because it tends to be the case that we call the same people,

  • even if we're using different phones.

  • And so by virtue of the fact that this number seems

  • to be calling this number and this number all the time,

  • like maybe it's my work line and my family, my home number.

  • If I'm always calling those two numbers, even if the phone number

  • changes, a pattern can still be established with the device IDs of all

  • of the other phones, maybe my regular phone plus all the burners that I've

  • had, where you can still craft a picture of who I am,

  • even though I'm using different devices, based on the call patterns

  • that I'm making.

  • As usual, humans are the vulnerability here.

  • Humans are going to use the same-- they're going to call the same people

  • and talk to the same people on their phones all the time.

  • And so it's relatively easy for mobile devices to track our locations.

  • Again, every device has a unique ID.

  • You can't hide that ID.

  • That ID is part of something that gets transmitted to cell towers.

  • And potentially the threat exists that if somebody

  • is able to break into that phone, whether that's

  • because of old, outdated firmware that's not been updated

  • or because of the potential that there is some sort of backdoor that

  • would allow an agent, authorized or not, to access it, again,

  • this vulnerability exists.

  • How does the law deal with do you own the information that is being tracked?

  • Do you want that information to be available to other people?

  • It's an open question.

  • Another issue at the forefront of where we're going,

  • especially when it comes to legal technology and law firms itself

  • availing itself of technology, is artificial intelligence and machine

  • learning.

  • Both of these techniques are incredibly useful potentially

  • to law firms that are trying to process large amounts of data

  • relatively quickly, the type of work that's

  • generally been outsourced to contract attorneys or first-year associates

  • or the like.

  • First of all, we need to define what it means when

  • we talk about artificial intelligence.

  • Generally when we think about that, it means

  • something like pattern recognition.

  • Can we teach a computer to recognize specific patterns?

  • In the case of a law firm, for example, that might be can

  • it realize that something looks like a clause in a contract, a valid clause

  • that we might want to see or a clause that we're

  • hoping not to see in our contracts.

  • We might want to flag that for further human review.

  • Can the machine make a decision about something?

  • Should it, in fact, flag that for review?

  • Or is it just highlighting things that might be alarming or not?

  • Can it mimic the operations of the human mind?

  • If we can teach a computer to do those things--

  • we've already seen that we can teach a computer

  • to teach itself how to reproduce bugs.

  • We saw that in Ken Thompson's compiler example.

  • If we can teach a computer to mimic the types of things

  • that we would do as humans, that's when we've

  • created an artificial intelligence.

  • There's a lot of potential uses for artificial intelligences

  • in the legal profession, like I said, document review being

  • one potential avenue for that.

  • And there are a few different types of ways that artificial intelligences can

  • learn.

  • There are actually two kind of prevailing major ways.

  • The first is for humans to supply some sort of data

  • and also supply the rules that map the data to some outcome.

  • That's one way.

  • The other way is something called neuroevolution,

  • which is generally best exemplified by way of a genetic algorithm.

  • In a moment, we'll take a look at a genetic algorithm literally written

  • in Python, where a machine learns over time to try and generate

  • the right result.

  • In this model, we give the computer a target, something

  • that it should try and achieve, and request

  • that it generates data until it can match

  • that target that we are looking for.

  • So by way of example, let's see if we can

  • teach a computer to write Shakespeare.

  • After all, it's a theory that given an infinite amount of time,

  • enough monkeys could write Shakespeare.

  • Can we teach a computer to do the same?

  • Let's have a look.

  • So it might be a big ask to get a computer to write all of Shakespeare.

  • Let's see if we can get this computer to eventually realize

  • the following line, the target, so to speak, "a rose by any other name."

  • So we're going to try and teach a computer.

  • We want a computer to eventually on its own

  • arrive at this phrase using some sort of algorithm.

  • The algorithm we're going to use to do it is called the genetic algorithm.

  • Now, the genetic algorithm is called this based on the theory of genetics,

  • that best traits or good traits will propagate down and become

  • part of the defined set of traits we usually encounter.

  • And bad traits, things that we don't necessarily want,

  • will be weeded out of the population.

  • And over successive generations, hopefully only the good traits

  • will prevail.

  • Now, just like any other genetic variation,

  • we need to account for a mutation.

  • We need to allow things to change.

  • Otherwise we may end up in a situation where all we

  • have is the potential for bad traits.

  • We randomly might need something to happen to eliminate that bad trait.

  • We have no other way to do it.

  • So we do have to mutate some of our strings from time to time.

  • How are we going to teach the computer to do this?

  • We're not providing it with any data set to start with.

  • The computer's going to generate its own data set, trying to get at this target.

  • The way we're going to do this is to create a bunch of DNA objects.

  • DNA objects, in this example, we're just going to refer to as different strings.

  • And the strings are just a random--

  • as exemplified here in this code, a random set of characters.

  • We're going to have it randomly pick.

  • I believe that the string's about 23 characters long

  • that we're trying to have it match.

  • So it's going to randomly pick 23 characters,

  • uppercase letters, lowercase letters, numbers, punctuation marks,

  • doesn't matter, any legitimate Ascii character,

  • and just add itself to the list of potential candidates

  • for the correct phrase.

  • So randomly slam on your keyboard and hit 23 keys.

  • The computer has about 1,000 of those to get started.

  • Every one of those strings, every one of those DNA items,

  • also has the ability to determine how fit it is.

  • Fitness being is it more likely to go on to the next generation?

  • Does it have characteristics that we might want to propagate down the line?

  • So for example, the way we're going to, in a rudimentary way,

  • assess the fitness of a string, how close it is basically to the target,

  • is to go over every single character of it and compare,

  • does this match what we expect in this spot?

  • So if it starts with a T--

  • or excuse me, starts with an A, "a rose by any other name,"

  • if it starts with an A, then that's one point of fitness.

  • If the next character is a space, then that's one point of fitness.

  • So a perfect string will have all of the characters in the correct space.

  • But as long as it has even just one character in the correct space,

  • then it is considered fit.

  • And so we iterate over all of the characters in the string

  • to see if it is fit.

  • Now, much like multiple generations, we need the ability to create new strings

  • from the population that we had before.

  • And so this is the idea of crossover.

  • We take two strings.

  • And again, we're just going to arbitrarily decide

  • how to take two strings and mash them together.

  • We're going to say the first half comes from the mother string,

  • and the second half comes from the father string.

  • And that will produce a child, which may have some positive characteristics

  • from the mother and some positive characteristics

  • from the father, which may then make us a little bit closer towards this idea

  • of having the perfect string.

  • Again, the idea here is for the computer to evolve itself

  • into the correct string rather than us just giving it a set of data

  • and saying, do this.

  • We want to let it figure it out on its own.

  • That's the idea of the genetic algorithm.

  • So we're going to arbitrarily split the string in half.

  • Half the characters, or genes of the string, come from the mother.

  • The other half come from the father.

  • They get slammed together.

  • That is a new DNA sequence of the child.

  • And then again, to account for mutation, we

  • need some random percent of the time, in this case, we're saying less than 1%

  • the time, we would like one of those characters to randomly change.

  • So it doesn't come from the mother or the father string.

  • It just randomly changes into something else, in the hopes

  • that maybe that mutation will be beneficial somewhere down the line.

  • Now, in this other Python file, script.py,

  • we're actually taking those strings that we are just randomly creating--

  • those are the DNA objects from the previous file--

  • and starting to actually evolve them over time.

  • So we're going to start out with 1,000 of these random strings.

  • And the best score so far, the closest score we have,

  • the best match to "a rose by any other name" is currently zero.

  • No string is currently there.

  • We may randomly get it on the first generation.

  • That would be a wonderful success.

  • It's pretty unlikely.

  • Population here is just an array.

  • It's going to allow us to store all of these 1,000 strings.

  • And then as long as we have not yet found the perfect string.

  • The one that has 100% fitness or a score of exactly 1,

  • we would like to do the following, calculate the fitness score

  • for every one of those random 1,000 strings that we generated.

  • Then, if what we just found is better than anything we've seen before--

  • and at the beginning, we start with zero,

  • so everything is better than what we've seen before, as long as it

  • matches at least one character--

  • then print out that string.

  • So this is a sense of progression.

  • Over time we're going to see the strings get better and better and better.

  • Then we're going to create what's called a mating pool.

  • Again, this is this idea of two strings sort of crossing over.

  • They're sort of breeding to try and create a better subsequent string.

  • Depending on how good that string is, we may

  • want that child to be in the next population more times.

  • If a string is a 20% match, that's pretty good, especially

  • if it's an early generation.

  • So we may want that string to appear in the mating pool, the next generation,

  • 20% of the time.

  • It has a better likelihood than a string that matches 5% of the characters

  • to be closer to the right answer.

  • So a string that barely matches anything,

  • sure, it should be in the pool.

  • Maybe it has the one character that we're looking for.

  • But we only want it in the pool 5% of the time

  • versus the string that matches 50% of the characters.

  • We probably want that in the pool 50% of the time.

  • The idea is, again, taking the best representatives of the next generation

  • and trying to have the computer learn and understand that those are good

  • and see if they can build better and better strings from those better

  • and better representatives of the population that

  • are more close to the target string that we're looking

  • for, "a rose by any other name."

  • Then in here all we're doing is picking two random items

  • from that pool we've just created of the best possible candidates

  • and mating those two together and continuing

  • this process of hopefully getting better and better approximations

  • of this string that we're looking for.

  • And what's going to happen there is they're going to create a crossover.

  • That crossover child DNA string will mutate into some other new string.

  • And we'll add that to the population to be considered for the next round.

  • So we're just going keep going over and over and over,

  • generating hopefully better and better strings.

  • So that's how these two files interact.

  • The first file that we took a look at defines the properties of a string

  • and how it can score itself basically.

  • And this process here in script.py--

  • and this these two files are based on a Medium post, which

  • we've described in the course materials, as well as an exam question that we've

  • previously asked in the college version of CS50,

  • for students to implement and solve on their own.

  • Hopefully these two files taken together, the script file,

  • will actually go through the process of creating this generation over and over.

  • So let's see this in action.

  • Let's see how in each successive generation

  • we see strings get closer and closer and closer to the target string.

  • Again, we never told the computer-- we never

  • gave the computer a set of starting data to work with, only an end goal.

  • The computer needs to learn how to get closer

  • and closer to finding the right string.

  • And that's what we do here.

  • So let's run our program and see if we've actually taught the computer how

  • to genetically evolve itself to figure out this target string

  • that we're looking for.

  • So we're going to run script.py, which is the Python file where

  • we described the process happening.

  • And let's just see how the generations evolve over time.

  • So we get started, and we have some pretty quick results.

  • This first string here has a matching score of 0.042, so 4%, which I believe

  • is one character.

  • So if we scroll through, we try and find "a rose by any other name,"

  • I don't know exactly which character it is here.

  • But this is basically saying one.

  • One of these characters matches.

  • It's 4.2% what we're hoping for.

  • That means that in the next pool, the next iteration,

  • this string will be included 4.2% of the time.

  • And there may also be other strings that also match.

  • Remember, we're only printing out when we have a better string.

  • So this only going to get included 4.2% of the time.

  • But there are going to be plenty of other things

  • that are also 4.2% matches that are probably matching-- each one of them

  • matches one different character.

  • So those will comprise part of the pool.

  • Then we're going to cross pollinate.

  • We're going to take each of those strings

  • that each had a one character match and mash them together.

  • Now, if the first string that we're considering

  • has the character match in the first half,

  • and the second string has a character match in the second half,

  • now we've created a new string that has two matches, right?

  • We know one of them was in the first half.

  • That came from the mother string.

  • We have one of them in the second half that came from the father's string.

  • And so the combined string together, unless that character

  • happens to get mutated out, which is a possibility--

  • we might actually take a good thing and turn it into a bad character.

  • Then the next one should be twice as good.

  • It should be 8.3% or 8.4% likely.

  • And that's exactly what it is.

  • So this next string has two matches.

  • And the next one has three and four.

  • And as we kind of scroll down, we see some patterns like this,

  • A question mark Q Y. That's obviously not part of the correct answer.

  • But it suggests that there's a parent in here that has this string that

  • tends to have really good fitness.

  • Like this string probably has many other characters outside of this box here

  • that match.

  • And so that parent propagates down the line for a while

  • until eventually those characteristics, in about the ninth generation or so,

  • get kind of wiped out.

  • And as we can see over time, what starts out

  • as a jumbled mess gets closer and closer to something

  • that is starting to look even at 58% like we're getting pretty close to

  • "a rose by any other name."

  • And as we go on and on, again, the likelihood gets better and better.

  • So that by the time we're here, at this line here,

  • this string is going to appear in 87 and 1/2%

  • of the next generation's population.

  • So a lot of these characteristics of this string that's close but not

  • exactly right will keep, appearing which makes it more and more likely

  • that it will eventually pair up with another string that

  • is a little bit better.

  • And as you probably saw, towards the end, this process got slower, right?

  • If all the strings are so good, it might just

  • take a while to find one where the match is better than the parents.

  • It might be the case that we are creating

  • combinations that are worse again.

  • We want to filter those back out.

  • And so it takes a while to find exactly what we're looking for.

  • But again, from this random string at the very beginning, over time,

  • the computer learns what parts are good.

  • So here's "rose," right, as part of the string.

  • This was eventually correct.

  • This got rooted out in the next generation.

  • It got mutated out by accident.

  • But mathematically, what it found was a little bit better.

  • There are more characters in this string that are correct than this one,

  • even if there are some recognizable patterns in the former.

  • But the computer has learned, evolved over time what it

  • means to match that particular string.

  • This is the idea of neuroevolution, teaching a computer

  • to recognize patterns without necessarily telling it

  • what those patterns are, just what the target should be.

  • So that genetic algorithm is kind of a fun programming activity.

  • But the principles that underpin it still apply to a legal context.

  • If you teach a computer to recognize certain patterns in a contract,

  • you can teach a computer to write contracts

  • potentially that match those patterns.

  • You can teach a computer to recognize those patterns

  • and make decisions based on them.

  • So we were using neuroevolution to build or construct something.

  • But you can also use neuroevolution to isolate correct sets of words

  • or correct sets of phrases that you're hoping to see in a contract

  • or that you might want to require for additional use.

  • So again, the types of legal work that this can be used to help automate

  • are things like collation, analysis, doing large document review,

  • predicting the potential outcome of litigation

  • based on having it review case precedents and outcomes

  • and seeing if there are any trends that appear in cases X, Y, and Z all

  • had this outcome.

  • Is there some other common thread in cases

  • X, Y, and Z that might also apply to the case that we're about to try?

  • Or potentially we need to settle because we see that the outcome is

  • going to be unfavorable to us.

  • But does this digital lawyering potentially make you uncomfortable?

  • Is it OK for legal decisions to be made by a computer?

  • Is it more OK if those decisions are made because we've trained them

  • with our own human instincts?

  • There are services out there.

  • There's a famous example of a parking ticket clearing service called Do Not

  • Pay from several years ago, where a 19- or 20-year-old computer

  • programmer basically taught a computer how

  • to argue parking tickets on people's behalf

  • so that they wouldn't have to hire attorneys to do so.

  • He wasn't a trained attorney himself.

  • He just recognized some of the things that are--

  • he talked to people and recognized some of the things that

  • are common threads for people who successfully challenged

  • parking tickets versus don't successfully challenge parking tickets,

  • taught a computer to mimic those patterns,

  • and have the computer send out notices and the like to defend parking

  • ticket holders.

  • And he was able to--

  • I think it was several hundred thousand dollars in potential legal fees saved

  • and several hundred thousand parking tickets that

  • were challenged successfully.

  • And the case was dropped, and there was no payment required.

  • So is it OK for computers to be making these decisions if humans teach them?

  • Is it only OK for computers to make those decisions

  • if the humans teaching them have legal training at the outset in order

  • to make these decisions?

  • Or can we trust programmers to write these kinds of programs for us as well?

  • Does lawyering rely on a gut instinct?

  • I'm sure sometimes in cases you've experienced

  • in your own practice the decision that you

  • make might be contrary to what you think might be the right thing

  • to do because you just feel like if I do this other thing

  • it's going to work better in this case.

  • And I'm sure that for many of you, this has paid off successfully.

  • Doing something that is in contravention of the accepted norm

  • is something that a computer may not be-- you

  • may not be able to train a computer to do that.

  • You may not be able to train gut instinct to challenge the rules,

  • when all this whole idea of neuroevolution and machine

  • learning and AI is designed to have computers learn and enforce rules.

  • Will the use of AI affect the attorneys' bottom line?

  • Hypothetically it should make legal work cheaper.

  • But this would then potentially reduce firm profits

  • by not having attorneys, humans, reviewing this material.

  • This is, in some ways, a good thing.

  • It makes things more affordable for our clients.

  • This is in some ways a bad thing.

  • We have entrenched expenses that we need to pay that are based on certain monies

  • coming in because of the hourly rates of our associates and our partners.

  • Does this change that up?

  • Does the fact of this changes it up, is it problematic?

  • Is it better for us to provide the most competent representation that we can,

  • even if that competent representation is actually from a computer?

  • Remember that as attorneys, we have an ethical obligation to stay on top of

  • and understand technology.

  • Sometimes that may become a situation where using that technology

  • and working with that technology really forces

  • us to do something we might not want to do

  • because it doesn't feel like the right thing

  • to do from a business perspective.

  • Nevertheless our ethical obligations compel us to potentially do that thing.

  • So we've seen some of the good things that machine learning can do.

  • But certainly there are also some bad things that machine learning can do.

  • There's an article that we provided about machine bias and a computer

  • program that is ostensibly supposed to be used by prosecutors and judges

  • when they are considering releasing somebody on bail

  • or setting the conditions for parole, whether or not

  • they're more likely to commit future crimes.

  • Like, what is their likely recidivism rate?

  • What kind of additional support might they need upon their release?

  • But it turns out that the data that we're feeding into these algorithms

  • is provided by humans.

  • And unfortunately these programs that are

  • supposed to help judges make better decisions have a racial bias in them.

  • The questions that get asked as part of figuring out

  • whether this person is more likely or not to commit a future crime,

  • they're never outright asking the question, what is your race

  • and basing a score on that.

  • But they're asking other questions that sort of are hints or indicators of what

  • someone's race might be.

  • For example, they're asking questions about socioeconomic status

  • and languages spoken and whether or not parents have ever

  • been imprisoned and so on.

  • And these programs sort of stereotype people in ways that are not OK,

  • or we might not deem to be OK in any way, to make decisions.

  • And these stereotypes are created by humans.

  • And so we're actually teaching the computer bias in this way.

  • We're supplying data.

  • We, as humans, are providing it.

  • We're imparting our bias into the program.

  • And the program is really just implementing

  • exactly what we're telling it to do.

  • Computers, yes, they are intelligent.

  • We can teach them to learn things about themselves.

  • But at the end of the day, that knowledge comes from us.

  • We are either telling them to hit some target or providing data to them

  • and telling them these are the rules to match.

  • So computers can are only as intelligent as the humans who create and program

  • them.

  • And unfortunately that means they're also as affected by bias

  • as the humans who create and program them.

  • These programs have been found that they are only 20%

  • of the time accurate in producing and predicting future violent crimes.

  • They are only 60% of the time accurate in predicting

  • any sort of future crime, so misdemeanors and so on,

  • so a little bit better than a 50/50 shot at getting it right

  • based on these predictive questions that they're asking people when

  • during intake process.

  • Proponents of these scoring metrics say that they provide useful data.

  • Opponents say that the data is being misused.

  • It's being used as part of sentencing determinations

  • rather than what its ostensible purposes, which

  • is to set conditions for bail and set conditions

  • for release, any sort of parole conditions that might come into play.

  • These calculations are also done by companies

  • that generally are for-profit entities.

  • They sell these programs to states and localities for a fixed rate per year

  • typically.

  • Does that mean that there's a financial incentive to make certain decisions?

  • Would you feel differently about these programs

  • if they were not free versus paid programs?

  • Should computers be involved in making these decisions that humans

  • would otherwise make anyway?

  • Like, given a questionnaire, would a human being

  • potentially reach the same conclusion?

  • Ideally that is what it should do.

  • It should be mimicking the human decision-making process.

  • Is it somehow less slimy feeling, for lack of a better phrase,

  • if a human being, a judge or a court clerk,

  • is making these determinations rather than a computer?

  • Now, granted the judge is still making the final call.

  • But the computer is printing out likely recidivism scores

  • and printing out all this data about somebody

  • that surely is going to influence the judge's decision

  • and in some localities, perhaps over influencing the judge's decision,

  • taking the human element out of it entirely.

  • Does it feel better if the computer is out of that equation entirely?

  • Or is it better to have a computer make these decisions

  • and potentially prevent mistakes from happening prevent or draw attention

  • to things that might otherwise be missed or minimize things that might otherwise

  • have too much attention drawn to them?

  • Again, a difficult question to answer, how much do we

  • want technology to be involved in the legal decision-making process?

  • But as we go forward, it's certainly undoubtedly true

  • that more and more decisions in a legal context

  • are going to be made by computers at the outset,

  • with humans sort of falling into the verification category rather

  • than active decision maker category.

  • Is this good?

  • Is this bad?

  • It's the future.

  • For entities based in the United States or who

  • solely have customers in the United States,

  • this next area may not be a concern now but it's very likely

  • to potentially become one in the future.

  • And that is what to do with GDPR, the General Data Protection

  • Regulation, or General Data Privacy regulation

  • that was promulgated by the European Union

  • and came into effect in May of 2018.

  • This basically defines the right for people to know what kind of data

  • is being collected about them.

  • This is not a right that currently exists in the United States.

  • And it'll be really interesting to see whether the EU

  • experiment about revealing this kind of data, which has never

  • been available to individuals before, will become something

  • that exists in the United States and is going to be something

  • that we have to deal with.

  • If you're based in the United States, and you do have customers in Europe,

  • you may be subject to the GDPR.

  • For example, us at CS50, we have students

  • who take the class through at edX, or HarvardX, the online MOOC platform.

  • And when GDPR took effect in May of 2018, we spoke to Harvard

  • and figured out ways that we needed to potentially interact

  • with European users of our platform, despite the fact that we're

  • based in the United States, and what sort of data implications

  • that might have.

  • And that it could be because of it's out of an abundance of caution to make sure

  • we're on the right side of it, even if we're not

  • necessarily subject to the GDPR, but it is certainly

  • an area of evolving concern for international companies.

  • The GDPR allows individuals to get their personal data.

  • That means data that either could identify an individual, something

  • like what we discussed earlier in terms of cookies and tracking

  • and the kinds of things that you search being tied to your IP address, which

  • then might be tied to your actual address and so on,

  • or data that even could identify an individual

  • but doesn't necessarily identify somebody just yet.

  • The requirement itself imposes requirements.

  • The regulation itself imposes requirements

  • on the controller, so the person who is providing a service

  • or is holding all of that data, and basically

  • says that what the controllers responsibilities are

  • for processing that data and what they have to reveal to users who request it.

  • So for example, on request, by a user of a service,

  • when that user and the controller are subjects the GDPR,

  • the controller must identify themselves, who they are,

  • what the best way is to contact them, tell the user what data they have

  • about them, how that data is being processed,

  • why they are processing that data, so what sorts of things

  • are they trying to do with it.

  • Are they trying to make longitudinal connections between different people?

  • Are they trying to collect it to sell it to marketers and so on?

  • They need to tell them if that data is going to be referred to a third party,

  • again, whether that's selling the data or using a third-party service to help

  • interpret that data.

  • So again for example, in the case of Samsung,

  • that might be Samsung is collecting your voice data.

  • But they may be sharing all the data they

  • get with a third party, whose focus, whose programming focus

  • is about processing that data and trying to find out better voice

  • commands by collecting the voices of hundreds of thousands

  • of different people so they can get a better

  • synthesis of a particular thing they hear, translating that into a command.

  • These same restrictions will apply whether the data

  • is collected or provided by the user, or is just inferred about the user

  • as well.

  • So that the controller would also need to reveal information

  • that was gleaned about somebody without necessarily having just

  • been given to them directly by the person providing that personal data.

  • The owner can also compel the controller to change data about them once they

  • get this report about what data they have about them that is inaccurate,

  • which brings up a really interesting question of, what if something

  • is accurate, but you don't like it, and you are

  • a person who's providing personal data?

  • Can you challenge it as inaccurate?

  • This is, again, something that has not been answered yet

  • but is very likely to be answered at some point by somebody.

  • What does it mean for data to be inaccurate?

  • Moreover, is it a good thing to delete data about somebody?

  • There are exceptions that exist in the GDPR for preserving data or not

  • allowing it to be deleted if it serves the public interest.

  • And so the argument that is sometimes made in favor of GDPR

  • is someone who commits a minor crime, for example,

  • might be haunted by this one mark on their record for years and years

  • and years.

  • They can never shake it.

  • And it's a minor crime.

  • There was no recidivism.

  • It wasn't violence in any way.

  • It just has now hampered-- it's impacted their life.

  • They can't get the kind of job that they want, for example.

  • They can't get the kind of apartment that they want.

  • Shouldn't they be able to eliminate that data?

  • Some people would argue yes, that the individual's already paid the price.

  • Society is not harmed by this crime or this past event any longer.

  • And so sure, delete that data.

  • Others would argue no, it's a part of history.

  • We don't have a policy of erasing history.

  • That's not what we do.

  • And so even though it's annoying perhaps to that individual,

  • or it's had a non-trivial impact on their life,

  • we can't just get rid of data that we don't like.

  • So data that might be deemed inaccurate personally,

  • like if a company gets a lot of information about me

  • because I'm doing a lot of online shopping, and they say,

  • I'm a compulsive spender, and that's part of their processed data,

  • can I challenge that is inaccurate because I

  • don't think I'm a compulsive spender?

  • I feel like I earn enough money and can spend this money how I want,

  • and it has an impact on my life negatively.

  • But they think, well, you've spent $20,000 on pictures of cats.

  • Maybe you are kind of a compulsive spender.

  • And that's something that we've gleaned from this data,

  • and that's part of your record.

  • Can I challenge that?

  • Open question.

  • For those of you who may be contending with the GDPR in your future practice,

  • we've excerpted some parts of it that are particularly relevant,

  • that deal with the technological implications

  • of what we've just discussed as part of the recommended

  • reading for this module.

  • The last subject that we'd like to consider in this course

  • is what is kind of a political hot potato right now in the United States.

  • And that is this idea of net neutrality.

  • And before we get into the back and forth of it,

  • I think it's properly important for us to define

  • what exactly net neutrality is.

  • At its fundamental core, the idea is that all traffic on the internet

  • should be treated equally.

  • We shouldn't prioritize some packets over others.

  • So whether your service is Google, Facebook, Netflix,

  • some huge data provider, or you are some mom-and-pop shop

  • in Kansas somewhere that has a few customers,

  • but you still have a website and a web presence,

  • that web traffic from either that location, the small shop,

  • or the big data provider should be treated equally.

  • One should not be prioritized over the other.

  • That is the basic idea that underpins-- when you hear net neutrality,

  • it is all traffic on the web should be treated equally.

  • The hot potato, of course, is, is that the right thing to do?

  • Let's try and visualize one way of thinking

  • about net neutrality that kind of shows you how both sides might perceive this.

  • It may help to think about net neutrality in terms of a road.

  • Much like a road has cars flowing over it,

  • the internet has information flowing over it.

  • So we can think about this like we have a road.

  • And proponents of net neutrality will say, well,

  • wait a minute, if we built a second road that was parallel to the first road,

  • went to the same place, but this road was maybe better maintained,

  • and you had to pay a toll to use it, proponents would say, hey, wait,

  • this is unfair.

  • All this traffic needs to use this main road

  • that we've been using for a long time.

  • But people who can afford to go into this new road, where

  • traffic moves faster, but you have to pay the toll, well, then

  • their traffic's going to be prioritized.

  • Their packets are to get there faster.

  • This is not fundamentally fair.

  • This is not the way the internet was designed,

  • where free flow of information is sort of priority,

  • and every packet is treated equally.

  • So proponents of net neutrality will say this arrangement is unfair.

  • Opponents of net neutrality, people who feel

  • like you should be able to have traffic that goes faster

  • on some roads than others, will say, no, no, no, this

  • is the free market talking.

  • The free market is saying, hey, if I really

  • want to make sure that my service gets to people faster,

  • I should have the right to do that.

  • After all, that's how the market works for just about everything else.

  • Why should the internet be any different?

  • And that's really the basic idea.

  • Is it should everybody use the same road,

  • or should people who can afford to use a different road be permitted to do so?

  • Proponents will say no.

  • Opponents will say yes.

  • That's the way the free market works.

  • From a theoretical perspective or from a technical perspective,

  • how would we implement this?

  • It's relatively easy if the service that we're trying to target

  • has paid for premium service.

  • Their IP addresses associated with their business.

  • And so the internet service provider, the people

  • who own the infrastructure on which the internet operates, so they literally

  • own the fiber optic cables along which the data operate,

  • can just say, well, any data that's going to this IP address,

  • we'll just prioritize it over other traffic.

  • There might be real reasons to actually want to prioritize other traffic.

  • So for example, if you are sending an email to somebody

  • or trying to access a website, there's a lot of redundancy built in here.

  • We've talked about TCP, for example, the Transmission Control Protocol,

  • and how it has redundancy built in.

  • If a packet is dropped, if there's so much network

  • congestion because everybody's flowing along that same road,

  • if there's so much congestion that the packet gets dropped,

  • TCP will re-send that packet.

  • So services that are low impact, like accessing a website for some company

  • or sending an email to somebody, there's no real worry here.

  • But now imagine a service like you're trying

  • to make an international business video call

  • using Skype or using Google Hangouts, or you're

  • trying to stream a movie on Netflix or some other internet video streaming

  • provider.

  • Generally, those packets are not sent using TCP.

  • They're usually using a different protocol called

  • UDP, whose purpose in life is really just to get information to as quickly

  • as possible, but there's no redundancy.

  • If a package gets dropped, that packet gets dropped, so be it.

  • Now, imagine if you're having an international business call.

  • There's a lot of packets moving, especially if you're

  • having a call with Asia, for example.

  • Between the United States and Asia, that has to travel along that Pacific cable.

  • There's a lot of traffic that has to use that Pacific cable.

  • Wouldn't it be nice, advocates against net neutrality would say,

  • if the company that's providing that service

  • was able to pay to ensure that its packets had priority thus

  • reducing the likelihood of those packets being dropped,

  • thus improving the quality of the video call, thus generally providing,

  • theoretically again, a better service for the people who use it.

  • So it might be the case that some services just need prioritization.

  • And the internet is designed in such a way

  • that we can't guarantee or give them that prioritization.

  • Isn't that a reason in favor of repealing net neutrality,

  • making it so that people could pay for certain services that

  • don't work with redundancy and require just to get there quickly

  • and get there guaranteed over other traffic?

  • In 2015, the Obama administration, when the Federal Communications Commission

  • was Democratically controlled, voted in favor of net neutrality,

  • reclassifying the internet as a Title II communications service.

  • Meaning it could be much more tightly regulated by the FCC

  • and imposing this net neutrality requirement.

  • Two years later, when the Trump administration came into office,

  • President Trump appointed Ajit Pai, the current chairman of the FCC,

  • who basically said he was going to repeal the net neutrality rules that

  • had been set in place by the Obama administration.

  • And he did.

  • Those took effect in the summer of 2018.

  • So we're now back in this wild lands of net neutrality

  • is on the books in some places.

  • There are even states now who have state laws

  • that are designed to enforce this idea, this theory of net neutrality,

  • that you're now running into conflict with federal law.

  • So there's now this question of who wins out here?

  • Has Congress claimed this domain?

  • Can states set different rules from what Congress and what regulators

  • appointed by or delegated responsibility by Congress to make these decisions?

  • Can states do something different than that?

  • It is probably one of the most hot-button hot-potato issues

  • in technology and the law right now.

  • What is going to happen with respect to net neutrality?

  • Is it a good thing?

  • Is it a bad thing?

  • Is it the right thing to do for the internet?

  • To learn a bit more about net neutrality,

  • we've supplied as an additional reading a con take on net neutrality.

  • Generally you'd see pro takes about this in tech blogs.

  • But we've explicitly included a con take on why net neutrality should not

  • be the norm, which we really do encourage you to take a look at

  • and consider as you dive into this topic.

  • But those are just some of the challenges

  • that lie at the intersection of law and technology.

  • We've certainly barely skimmed the surface.

  • And my hope is that I've created far more questions than answers

  • because those are the kinds of questions that you

  • are going to have to answer for us.

  • Ultimately it is you, as practitioners, who

  • will go out and face these challenges and figure out

  • how we're going to deal with data breaches, how we're

  • going to deal with AI in the law, how we're

  • going to deal with net neutrality, how we're going to deal with issues

  • of software and trust.

  • Those are the questions for the future that lie at this intersection.

  • And the future is in your hands.

  • So help lead us in the right direction.

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