Anmerkungen:
In: 57 Georgia Law Review, 1669 (2023)
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 23, 2023 erstellt
Beschreibung:
Artificial intelligence, in the form of machine learning systems, is becoming widely deployed across many industries to facilitate the production of new technical or expressive works. Among other applications, these technologies promise rapid product design and creation, often exceeding the capacity of human creators. Commentators and policy makers have responded to these developments with a flood of literature analyzing the ways in which AI systems might challenge our existing regimes of intellectual property. But such discussions have thus far focused on entirely the wrong questions, misunderstanding the nature of the changes that AI brings to creative development. The history of intellectual property law is in some sense the history of falling technological costs; advances in manufacturing, communications, and transportation have successively lowered barriers to access and distribution of creative goods. Falling costs of appropriability mean diminished opportunity for profit, and so diminished incentives to invest in creative goods. Intellectual property law is typically justified today as an answer to the undersupply problems created by falling costs of marginal distribution; patent and copyright laws provide legal exclusion where physical exclusion is not feasible. The promise of legal exclusivity offers investors the opportunity to recoup investments in creative goods. But this incentive paradigm is upended by current trends in machine learning. Cost savings from AI systems occur at a different point in the production process. Rather than further lowering the cost of distribution, AI systems promise (or threaten) to lower the cost of initial development of creative goods, providing a synthetic substitute for human creativity. Their incorporation into creative production will in effect automate the generative phases of the creative development process, substantially lowering the cost of the initial stage of production. Like other cost-saving industrial automation, this can be expected to displace human labor and redefine human roles in production. The history of past automated labor displacements teaches us something of what will occur as creativity is automated. Consequently, in this paper I begin to reframe the discussion of intellectual property and artificial intelligence, showing the impact machine learning will have on human creativity and innovation, and the implications these changes for intellectual property doctrine and policy. In particular, I show that cheap substitutes for human creativity will drive a shift toward forms of intellectual property that certify authenticity rather than those that incentivize production and distribution. Armed with this understanding, we can begin to address the question of how to foster human engagement in an age of automated creativity