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Full-Text Articles in Law
Decoding U.S. Tort Liability In Healthcare's Black-Box Ai Era: Lessons From The European Union, Mindy Duffourc, Sara Gerke
Decoding U.S. Tort Liability In Healthcare's Black-Box Ai Era: Lessons From The European Union, Mindy Duffourc, Sara Gerke
Faculty Scholarly Works
The rapid development of sophisticated artificial intelligence (“AI”) tools in healthcare presents new possibilities for improving medical treatment and general health. Currently, such AI tools can perform a wide range of health-related tasks, from specialized autonomous systems that diagnose diabetic retinopathy to general-use generative models like ChatGPT that answer users’ health-related questions. On the other hand, significant liability concerns arise as medical professionals and consumers increasingly turn to AI for health information. This is particularly true for black-box AI because while potentially enhancing the AI’s capability and accuracy, these systems also operate without transparency, making it difficult or even impossible …
How Ai Can Learn From The Law: Putting Humans In The Loop Only On Appeal, I. Glenn Cohen, Boris Babic, Sara Gerke, Qiong Xia,, Theodoros Evgeniou, Klaus Wertenbroch
How Ai Can Learn From The Law: Putting Humans In The Loop Only On Appeal, I. Glenn Cohen, Boris Babic, Sara Gerke, Qiong Xia,, Theodoros Evgeniou, Klaus Wertenbroch
Faculty Scholarly Works
While the literature on putting a “human in the loop” in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an …