University of California Hastings College of the Law
UC Hastings Scholarship Repository (University of California, Hastings College of the Law)Not a member yet
18514 research outputs found
Sort by
Racial and Gender Bias in Child Maltreatment Reporting Decisions: Results of a Randomized Vignette Experiment
No full textIn this randomized vignette experiment, we asked 4,000 respondents through a YouGov survey to decide how likely they would be to report potential instances of child maltreatment to authorities. We used racialized and gendered names to suggest the identities of the parents and children in each of the ten vignettes that were based on real-life events. We find that respondents were less likely to report potential child maltreatment when the vignette used non-white names to describe the family participants. Respondents were less likely to report when a male child was involved, and more likely to report when a male parent was involved. The uncovered racial and gender biases were more pronounced in vignettes that were of intermediate severity. We conclude by offering normative implications and suggesting policy interventions to mitigate the effect of these biases
The Tragedy of the AI Anticommons
No full textShould AI companies be allowed to “train” their models on the copy- righted works of others without consent or compensation? Legally, can they? These questions are being litigated in courts across the United States right now. When a resource, such as AI, is engulfed in effective rights of exclusion from a vast array of battling rightsholders, that resource is susceptible to un- derutilization. This phenomenon is referred to as a tragedy of the anticom- mons. This Article highlights how AI is subject to an anticommons weak- ness. If the millions of intellectual property holders, whose intellectual property these AI models are trained on, all see their rights of exclusion be- come effective, it could signal the end of AI before we know it.
As the first Article to shine a light on the anticommons property at the intersection of AI and intellectual property, this narrow focus reveals multi- ple possible solutions to thwart a tragedy of the AI anticommons. Relying on the cross section of traditional entitlements theories and efficiency ration- ales, possible solutions such as private market-based actors and legislatively compulsory licensing emerge. In addition to exposing these pre-existing mechanisms, this Article goes one step further by demonstrating how trans- ferring untapped bankruptcy principles into the AI and intellectual property anticommons is the novel solution this problem needs. Utilizing trusts and channeling injunctions from bankruptcy law to prevent a tragedy of the AI anticommons is a unique approach that allows for a clarification of intellec- tual property entitlements and a reduction in bargain-based transaction costs