Algorithmic Fair Use
Legal governance and regulation are becoming increasingly reliant on datacollection and algorithmic data processing. In the area of copyright, online protec-tion of digitized works is frequently mediated by algorithmic enforcement systemsintended to purge illicit content and limit the liability of YouTube, Facebook, andother content platforms. But unauthorized content is not necessarily illicit content.Many unauthorized digital postings may claim legitimacy under statutory excep-tions like the legal balancing standard known as fair use. Such exceptions exist toameliorate the negative effects of copyright on public discourse, personal enrichment,and artistic creativity. Consequently, it may seem desirable to incorporate fair usemetrics into copyright policing algorithms, both to protect against automated over-deterrence and to inform users of their compliance with copyright law. In this Essay,I examine the prospects for algorithmic mediation of copyright exceptions, warningthat the design values embedded in algorithms will inevitably become embedded inpublic behavior and consciousness. Thus, algorithmic fair use carries with it thevery real possibility of habituating new media participants to its own biases and soprogressively altering the fair use standard it attempts to embody.