317 citations · 1.2k across the 26 of their papers we have counts for
5 papers · 1 filter
Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency
Abeba Birhane, Marek McGann
In this paper we argue that key, often sensational and misleading, claims regarding linguistic capabilities of Large Language Models (LLMs) are based on at least two unfounded assu…
The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models
Abeba Birhane, Sepehr Dehdashtian, Vinay Uday Prabhu +1
Scale the model, scale the data, scale the GPU farms is the reigning sentiment in the world of generative AI today. While model scaling has been extensively studied, data scaling a…
Debunking Robot Rights Metaphysically, Ethically, and Legally
Abeba Birhane, Jelle van Dijk, Frank Pasquale
In this work we challenge arguments for robot rights on metaphysical, ethical and legal grounds. Metaphysically, we argue that machines are not the kinds of things that may be deni…
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Victor Ojewale, Ryan Steed, Briana Vecchione +2
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains inc…
AI auditing: The Broken Bus on the Road to AI Accountability
Abeba Birhane, Ryan Steed, Victor Ojewale +2
One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical n…