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Degrees Of Confidence As A Legal Tool To Assess Ai System Liability, Joshua Song
Degrees Of Confidence As A Legal Tool To Assess Ai System Liability, Joshua Song
Michigan Technology Law Review
AI systems have become increasingly integrated into our everyday lives, and harms caused by these systems have graduated from raising hypothetical ethical concerns to questions of actual legal liability. Civil liability schemes are generally designed to address harms caused by humans; thus, it may be tempting to analogize new types of harms caused by AI systems to familiar harms caused by humans in order to justify commandeering existing human-centered legal tools to assess AI liability. However, the analogy is inappropriate and misrepresents salient legal differences in how harms are committed by humans and AI systems. Thus, “as is often the …
Unreasonable: A Strict Liability Solution To The Ftc’S Data Security Problem, James C. Cooper, Bruce H. Kobayashi
Unreasonable: A Strict Liability Solution To The Ftc’S Data Security Problem, James C. Cooper, Bruce H. Kobayashi
Michigan Technology Law Review
For over two decades, the FTC creatively employed its capacious statute to police against shoddy data practices. Although the FTC’s actions were arguably needed at the time to fill a gap in enforcement, there are reasons to believe that its current approach has outlived its usefulness and is in serious need of updating. In particular, our analysis shows that the FTC’s current approach to data security is unlikely to instill anything close to optimal incentives for data holders. These shortcomings cannot be fixed through changes to the FTC enforcement approach, as they are largely generated by a mismatch between the …