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Altruistic Arbitrage and Climate Change Mitigation:Rethinking the Role of Cap and Trade Policies
Get PDFWe examine the implications for efficient public goods provision by exploring the relationship of altruism to the endowment effect, focusing our analysis on the problem of climate change mitigation. We argue that the reduction in distortionary valuation (i.e., willingness-to-accept departing from willingness-to-pay) experienced by altruistic market participants implies an ability to mediate ignored trades and extract the gains from trade — an activity we call altruistic arbitrage — thereby improving the efficiency of markets. This activity, broadly speaking, restores the Coase Theorem in the context of WTA-WTP disparities. Moreover, it leads to previously unidentified benefits to when markets are employed to protect and foster public goods. On the basis of our findings, we recommend that market maker licenses be auctioned as a novel means to implement cap and trade markets for reducing the long-term effects of climate change
Beyond Patents: Incentive Strategies for Ocean Plastic Remediation Technologies
Get PDFWith a garbage truck’s worth of plastic being dumped in the ocean each minute, there is a dire need for effective technological solutions aimed at mitigating the marine plastic pollution problem. However, the reliance of the U.S. patent system on market demand to incentivize this type of innovation has proven insufficient in light of the peculiarities of “green” technologies. To remedy this, this article proposes a multi-faceted incentivization approach that looks beyond the U.S. Patent and Trademark Office to stimulate the development of remediation technologies through comprehensive regulatory interventions, the establishment of prize funds and other alternative incentive mechanisms, and targeted reforms to patent procedures
The Right to a Glass Box: Rethinking the Use of Artificial Intelligence in Criminal Justice
Get PDFArtificial intelligence (“AI”) increasingly is used to make important decisions that affect individuals and society. As governments and corporations use AI more pervasively, one of the most troubling trends is that developers so often design it to be a “black box.” Designers create AI models too complex for people to understand or they conceal how AI functions. Policymakers and the public increasingly sound alarms about black box AI. A particularly pressing area of concern has been criminal cases, in which a person’s life, liberty, and public safety can be at stake. In the United States and globally, despite concerns that technology may deepen pre-existing racial disparities and overreliance on incarceration, black box AI has proliferated in areas such as: DNA mixture interpretation; facial recognition; recidivism risk assessments; and predictive policing. Despite constitutional criminal procedure protections, judges have often embraced claims that AI should remain undisclosed in court.
Both champions and critics of AI, however, mistakenly assume that we inevitably face a trade-off: black box AI may be incomprehensible, but it performs more accurately. But that is not so. In this Article, we question the basis for this assumption, which has so powerfully affected judges, policymakers, and academics. We describe a mature body of computer science research showing how “glass box” AI—designed to be fully interpretable by people—can be more accurate than the black box alternatives. Indeed, black box AI performs predictably worse in settings like the criminal system. After all, criminal justice data is notoriously error prone, and it may reflect preexisting racial and socioeconomic disparities. Unless AI is interpretable, decisionmakers like lawyers and judges who must use it will not be able to detect those underlying errors, much less understand what the AI recommendation means.
Debunking the black box performance myth has implications for constitutional criminal procedure rights and legislative policy. Judges and lawmakers have been reluctant to impair the perceived effectiveness of black box AI by requiring disclosures to the defense. Absent some compelling—or even credible—government interest in keeping AI black box, and given the substantial constitutional rights and public safety interests at stake, we argue that the burden rests on the government to justify any departure from the norm that all lawyers, judges, and jurors can fully understand AI. If AI is to be used at all in settings like the criminal system—and we do not suggest that it necessarily should—the presumption should be in favor of glass box AI, absent strong evidence to the contrary. We conclude by calling for national and local regulation to safeguard, in all criminal cases, the right to glass box AI
Section 702 Surveillance: Where Do We Go From Here?
No full textDiscussants:
Prof. Shane Stansbury, Robinson Everett Distinguished Fellow in the Center for Law, Ethics, and National Security Senior Lecturing Fellow, Duke Law
Mr. Ben Kastan, Senior Counsel, Data Protection and Cybersecurity at Visa, Global Privacy Offic
Conference Leadership Speech: LOAC in the 21st Century Battlespaces
No full textSpeaker: BG David E. Mendelson, USA, Assistant Judge Advocate General for Military Law and Operation
