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.

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