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Articles 1 - 4 of 4
Full-Text Articles in Law
An Intelligent Path For Improving Diversity At Law Firms (Un)Artificially, Rimsha Syeda
An Intelligent Path For Improving Diversity At Law Firms (Un)Artificially, Rimsha Syeda
Michigan Technology Law Review
Most law firms are struggling when it comes to diversity and inclusion. There are fewer women in law firms compared to men. The majority of lawyers—81%—are White, despite White people making up only about 65% of the law school population. Lawyers of color remain underrepresented with the historic high being only 28.32%. By comparison, 13.4% of the United States population is Black and 5.9% is Asian. The biases that perpetuate this lack of diversity in law firms begin during the hiring process and extend to associate retainment. For example, an applicant’s resume reveals a lot, including the prestige of the …
Equitable Ecosystem: A Two-Pronged Approach To Equity In Artificial Intelligence, Rangita De Silva De Alwis, Amani Carter, Govind Nagubandi
Equitable Ecosystem: A Two-Pronged Approach To Equity In Artificial Intelligence, Rangita De Silva De Alwis, Amani Carter, Govind Nagubandi
Michigan Technology Law Review
Lawmakers, technologists, and thought leaders are facing a once-in-a-generation opportunity to build equity into the digital infrastructure that will power our lives; we argue for a two-pronged approach to seize that opportunity. Artificial Intelligence (AI) is poised to radically transform our world, but we are already seeing evidence that theoretical concerns about potential bias are now being borne out in the market. To change this trajectory and ensure that development teams are focused explicitly on creating equitable AI, we argue that we need to shift the flow of investment dollars. Venture Capital (VC) firms have an outsized impact in determining …
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 …