Wednesday, January 30 at 12:00pm to 1:00pm
Law Building (LAW), 3500
401 East Peltason Drive Irvine, CA 92697-8000
Dan Burk, Chancellor's Professor of Law, UCI School of Law, will be presenting his paper, "Copyright and the Algorithmic Assemblage".
Automated decision-making, coupled with data profiling, is increasingly being deployed to mediate or to assist in legal determinations across a range of domains including corporate law, criminal law, contract, and tort. In the area of copyright, “Big Data” profiling proposals include the personalized modulation of infringement liability based on consumer market profiles. This work postulates using a consumer’s algorithmically determined willingness to pay as a metric to assign liability for copyright infringement. If the protected content were available only at a price higher than the consumer’s algorithmically determined willingness to pay, no liability would accrue for copying the work. Conversely, if the protected work were available at or below the consumer’s expected willingness to pay, liability would attach.
However, an increasingly robust sociological literature on human interaction with algorithms demonstrates that such approaches will likely distort the markets in which they are applied. Consequently, in this paper, I begin to map out the intersection between the social construction of markets and the social construction of data profiles in the context of intellectual property law. I begin by examining the problematic assumptions that economic consumer metrics bring to copyright. I then turn to consider the use of algorithmic data processing in determining such metrics, outlining first the decontextualized nature of algorithmic data ingestion, and then the strong social reflexivity effects associated with algorithmic scoring.
When applied to copyright liability, these effects can be expected to categorically re-structure both markets and market actors associated with copyright. I further suggest that when taken as a metric for judicial determinations of liability, the social effects of algorithmic categorization can be expected to generate unexpected and perverse outcomes. Thus, reliance upon algorithmic consumer scoring is not merely problematic for copyright policy, and has implications not only for algorithmically determined copyright liability, but for the use of algorithmic metrics in other areas of law as well.
2018-2019 Intellectual Life Workshop Calendar
Lunch will be provided.
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