Thursday, August 09, 2018

IPSC session 3 (trade secret then copyright)

Session 3:
Trade Secrets, Courtney Cox, Can the Law Force You to Lie? The Use of Deceptive Precautions to Protect Trade Secrets

Reasonable measures to protect the secret are generally required. Should you have to engage in deception, or deceptive misdirection (answering a related question but not what was asked, or answering partially as if it were fully) to be reasonable?  Lying/misinformation can cause real harms to others, if they rely on your representation to do something, and there are dignitary harms in being lied to/treated as an object.  Concerns about trust: if everyone lies when it’s convenient, then representations become incredible.

Companies do in fact lie to protect trade secrets.  E.g., putting out that security officers are listening at the local bar.  Or putting in a deliberate error in a map to identify copiers.  How I Met Your Mother: to protect the finale, they mislabeled scripts/casting calls; Game of Thrones films fake scripts so the cast doesn’t necessarily know what will be shown.  Cisco found source code distributed (disgruntled employee) and rather than attracting attention via takedown it seems to have posted a bunch of different fake versions; this is designed to wear people out and get them to think it’s all fake.

There’s philosophical debate over how to define a lie. Some people bake into the concept the idea of wrongfulness, but she doesn’t want to do that: she means intentional assertions of fact that aren’t true. A lie can be deceptive, if it imparts the false belief.

Some of these techniques are definitely cheaper than demand letters/hiring a lawyer. Thus they could count as reasonable measures, alone or in combination with techniques.  But: (1) doctrine of unclean hands—you shouldn’t be able to get legal relief based on conduct that is itself inequitable/wrongful, which is often about deception; (2) risk of harm to others; (3) general distaste for misrepresentation; (4) immorality of lying no matter what. If trade secret is the codification of commercial morality, then it shouldn’t include this.

Harm: suppose a screenwriter thinks they’ve been hired to write a finale for Game of Thrones, and they therefore give up lucrative other opportunities. Is this relevant harm?  Should we compare this to other precautions that they should have taken?  You’re allowed to have guard dogs; if the guard dogs are unreasonably vicious, then the remedy is tort law.  Thus, torts should provide the remedy rather than being incorporated into trade secret law.  [Query whether there’s any scenario in which the screenwriter hasn’t contractually waived all rights here.]

Bottom line: yes, there is at least a not insignificant set of deceptive practices that should be okay/not actionably deceptive or unclean hands; there may be a subset that are required.  She would not draw the line at corrective, post-disclosure deception.  Car manufacturers: road tests of a new innovation, protecting from corporate spies with cameras. So they use car disguises: cardboard covering the innovation, or making it look like some other innovation was being tested so that it wouldn’t be properly identified.

Q: gov’t lies all the time, for (they think) good reasons—informants, cooperating witnesses, interrogation. Is there a salient difference b/t gov’t lying and gov’t rewarding lies through policy? Compelled speech considerations: posits that gov’t should never force someone to lie through doctrine even if lying is ok when chosen.  Reactions?

A: There is reason to think gov’t can do what civilians can’t, but that doesn’t cover the whole logical space of actions.  Rewarding might not be enough to compel—you don’t have to claim trade secret protection, but if you want to do so, you have to engage in speech, just as you would to get a professional license of many kinds.

IP and Creativity
Sean Pager, Much Ado About Norms
Issues w/suboptimal norms: if there’s not sufficient communication, people may think norm is popular even though everyone else only goes along b/c they think it’s a norm too—drinking a lot on college campuses, for example.  Became a norm b/c of a vocal minority. Powerful people can also impose norms against the true internal desires of most.  Ellickson’s Shasta farmers are all white men occupying lands stolen from Indians and often exploiting Latino workers.  How seriously should we take these concerns in IP norms?  This is a question worth asking.

One example: People have written about Nollywood, the Nigerian film industry, as a negative IP space. Early on, Nigerian filmmakers used lead time to make money before piracy overtook them; new tech has changed that.  Social norms/marketers guilds have replaced that with a de facto exclusivity norm. That meets the paradigm of social norms solving a gap in IP law, but is it an optimal norm?  He says no. Would be better off with a formal IP system. Guilds often discriminate ethnically, by gender, etc. Filmmakers want a formal system but have been politically blocked by the clout of the marketers—norm-locking.

Chris Sprigman: how important is it to the norms literature whether norms are efficient?  Varies across the literature. Rothman has work on this. Some is descriptive and also attempts to account for why the norms lead to the production of certain types of content and not others. Could categorize this literature in your discussion.

Jennifer Rothman: Lisa Bernstein has some work on this in merchant norms.  Inherent biases in relying on norms based system—pay equity and hiring; informality of the system can reify principles of discrimination. Not well described in the IP context w/r/t these norms.  That would be a meaningful intervention.

Rosenblatt: “good” and “efficient” are not the same thing. I could reasonably be accused of being overoptimistic about norms, but good v. destructive/counterproductive/discriminatory/otherwise suboptimal are different things. Effective governors of behavior, beneficial governors of behavior, good substitutes for law: these are different things from when norms are efficient. 

A: would also add: good/efficient for whom or for what?  [See Glynn Lunney on this Q in IP law generally, not on norms.]

Q: interesting work on architecture—fan fiction sites where the sharing models are designed by women, v. FB where all the creators of the sharing features are men.   [missed the name, want to know it!]

A: link there b/t code as law and norms as law.

Q: identify situations where formal law has replaced norms, or where there is demand to codify norms. Could happen for different reasons: norms might be suboptimal, or somebody lobbies to impose costs on someone else. Transitions are revealing situations.

Andres Sawicki, The Law of Creativity?

Lots of different models of creativity. Law’s traditional model: works are public goods, need incentives.  Categorization: (1) Motivational. Focus on the individual; psychology; motivation for creative behavior. (2) Environmental. Individual behavior isn’t the primary determinant; there’s a lot of serendipity, fortuity, unexpected results, play; what matters is the sociocultural environment in which the individual environment and so we should focus law on opportunities for serendipity.  There’s surface tension, so to speak—the agents operating in these models look very different. Homo economicus is completely unrecognizable in ordinary life; in the creativity models, we have rich portraits of humans operating in recognizable contexts.  There’s also tension in the role of IP law for the models. IP is central to the operation of the model in public goods conception, not so much with the others.  (1) and (2) tend to bash the rational actor’s lack of realism. 

Can we resolve this tension?  Respectable realism, from philosophy of science. There are lots of useful ways to look at a given phenomenon depending on what you’re interested in.  All models are wrong but some are useful, and it’s with that in mind that we should choose our models.

Could also abandon model realism more generally.  Model realism is an important natural sciences issue—are there “really” subatomic particles just because we can make/verify predictions that come from positing their existence?  As legal scholars we don’t need a grand unified theory, or to posit unique differences b/t intrinsic and extrinsic motivation, we just need to know what we want to do. What are we using these models for?  Generating testable predictions is something we want from models.  But we can also make interpretative uses: looking at hypothetical situations.  Models can also be built from the ground up, used as slightly more abstract representations of reality in order to organize our thinking/our world—that’s going on w/some of the models in the literature/critique of public goods model.  There’s no right way to do this.

Jeremy Sheff: Epistemological roots of those philosophical schools matter—American pragmatists, for example. When rubber hits road on doctrine, will intersect w/Legal Realism in important way b/c of the epistemological commitments in philosophy of science and how they map to Legal Realism.

A: His point is that we’re not there yet. Entirely possible that all these models and maybe more will provide useful insights. [Some pluralism about realism?  That would be a fantastic title, I think.]

Sheff: that’s a very pragmatist idea, but there are limits on how far a pragmatist take on epistemology can take you. If it’s about how our community defines truth, then the interest shifts to the definition of our community.

Christopher Sprigman, The Second Digital Disruption: Algorithms & Authorship in the Adult Entertainment Industry

First digital disruption: rise of content distribution on the internet—Napster disrupted the music industry. Now, with porn, content builds brands to sell other stuff: just as with Amazon, its video content is an inducement to get people to sign up for Prime.

Mindgeek started w/ rise of YouTube: pornographic user-uploaded videos. Destroyed the mom & pop outlets. Large financial backing enabled Mindgeek to use piracy to drive down the value of the mom & pop outlets and then buy them up.  Camming has stabilized as an experience good, hard to knock off—like monetizing live music performances.  There’s an enormous custom market now.

But the next thing that happens is Mindgeek’s dominance. Now that they control so much production and distribution, user data comes back to them, allowing them to kick off the second digital disruption centered on data-driven creation, if this is a harbinger of what is coming from Netflix and Spotify and Amazon: to shape the way content is presented and even made.  Netflix made House of Cards because their data told them that a group of consumers they wanted to court would like a BBC-like political show and would like Kevin Spacey.  What picture you see when you see a tile for The Crown is based on your responses to past ads for shows. Amazon is following in Netflix’s wake, greenlighting content and then strangling it in response to data. Spotify is interested in computer generated music.

Data changes the risk of failure. More data: maybe able to create fewer risks.  Can also indirectly address the risk of success (which is piracy).  Entities that engage in data driven creation tend to be big and to have an all you can eat model, which makes piracy less important. We might be able to have less copyright and get the same investment incentives because the risk of failure is less (so the incentive needs to be less) and the risk of piracy is also less.

Moral intuitions about copyright: labor theories.  Consumer preferences and choices might start to be understood as part of the contribution to the “work” that is produced. One model: The creator brings something to humanity, like Prometheus bringing fire: that myth is hard to sustain with data driven creativity. The creator is watching the watched, who are then watching their reflections.

Sheff: I should hate this trend, but not sure about what’s the problem.  (A: Feels manipulative.) Even if it is in this iterative way, you still need experiments: you need an A and a B for your A/B testing.  You don’t reach an equilibrium. Data driven model can’t seem to capture that.

A: lots of research tries to pin this down. Newness plays relatively modest role—we tend to like things that are a little bit new but not a lot. Not that different from what we actually like, but we just tell ourselves a story about what we like that doesn’t match with our real enjoyment.  He thinks of fashion: often the constant churning of the same stuff. Hard to make judgments about whether fashion is more or less creative than other fields. These Qs are above his pay grade.

[Jon Ronson’s The Butterfly Effect influenced my thinking here. Camgirls and customs are the opposite of algorithms: this story you’re telling is not really about algorithmic creation but about the immiseration of the middle class, where Mindgeek makes all the money and we’re back to cottage industry for everyone else.  Note that this content is mostly functional, and the average stay on the site is ten minutes; they don’t think they can give guys more orgasms or longer ones, do they?  So what is the data being used to shape content for? There are good reasons rooted in the structure and incentives of the firm why they might want to use the algorithms to direct the creation of porn and tell people that they are making better porn as a result, but it’s not obvious why this tells us about creating things that have more degrees of freedom/some aim other than producing an orgasm.  So, for example, I’m pretty sure a lot of big companies would prefer AI performers instead of cattle/actors [who might, you know, end up being Kevin Spacey]. But I also have a strong sense that AI performers won’t work for some significant subset of porn consumers, to whom it is important that a woman do something for them. I could be wrong about that, though.

Relatedly: Cui bono: and what is the relationship of who benefits to the content of what is produced? When I was growing up guys didn’t expect to come on our faces, and at least the reporting tells me that things have changed.

A: [partial] Interpretive qs are beyond my pay grade. [But if you’re telling us to draw conclusions about creativity from this example, that’s what you’re getting paid for.  We are privileged white people and some topics might not be our topics to publicize and pioneer the discussion on.] The paper is about a phenomenon of which the adult industry is the best exemplar, but Netflix and Spotify are part of the story. Camming and customs are happening and have nothing to do with data. We’re trying to show you where Mindgeek came from and our fundamental story about how the use of data may transform industries [but if you’re only telling half the story about what the industry is, then is that a useful account? It seems a lot like talking about work without talking about domestic, unpaid labor].

Rosenblatt: consider the things toddlers like to watch on YouTube—it’s not what you think. Normatively may be unclear/different.

Lemley: old enough to remember when cable was going to replace TV with ads b/c of the new business model.  In fact, it just gets layered on top of the existing revenue generation model. Fine w/normative suggestion that we should be fine w/piracy but as a practical matter, the business model is likely to be “here’s a new way to make money.” [Relatedly, dialing down on copyright dials down the only lever that the people in cottage industry production may have, which has serious distributional consequences.]

A: risk of failure going down changes incentive structure, and risk of success going down has indirect effects, suggesting recalibration could be appropriate.

Lemley: differential benefits for big producers which are the only ones who can do this at the necessary scale.  Small producers lose out.

A: this is what people accuse Mindgeek of having done—used piracy to drive down value of companies they acquired. TW/AT&T merger: TW made argument that they needed to be able to hook up to digital distribution in order to target ads/shape content and avoid competitive disadvantage w/Amazon and Netflix; the judge totally bought it.  Data now becoming more salient in creation of content: that’s a fundamental story. Whether this is good or bad is a new set of questions. Sounds like new issues of platform dominance, as w/FB.  That may be indeed what we’re facing. May make sense to oppose it and do something about it but we have to identify it first.

Immiserating the middle class: FastCompany CEO wants lots of middle class musicians, not people earning millions a year.  Self-serving vision but not normatively worse than opposite. Artists do benefit in surprising ways: bands on tour use Spotify data not only to decide where to tour but also what songs to play in what cities. In equilibrium, we should expect artists to do better at identifying and finding their audiences.

No comments: