Library
-
The lost art of mathematical modelling
June 28, 2023Abeba BirhaneAI fairness, accountability, and transparencyA critique of mathematical biology in light of rapid developments in modern machine learning.
-
Science in the age of large language models
April 26, 2023Abeba BirhaneAI fairness, accountability, and transparencyFour AI ethics and policy experts highlight the potential risks associated with LLMs along with the need for responsible usage and careful consideration.
-
Beyond Single-Mindedness: A Figure-Ground Reversal for the Cognitive Sciences
Jan. 10, 2023Abeba BirhaneAI fairness, accountability, and transparencyA letter to the editors of the Cognitive Science Journal from a transdisciplinary collective.
-
ChatGPT, Galactica, and the Progress Trap
Dec. 9, 2022Deb Raji, Abeba BirhaneAI fairness, accountability, and transparencyFor WIRED, the authors hone in on the need for accountability in LLM deployment.
-
Handling and Presenting Harmful Text in NLP Research
Dec. 1, 2022Abeba BirhaneAI fairness, accountability, and transparencyThe article provides advice for NLP practitioners, with concrete steps for mitigating harm in research and in publication, on how to handle, present, and discuss harmful texts.
-
The unseen Black faces of AI algorithms
Oct. 19, 2022Abeba BirhaneAI bias & discriminationFor Nature Journal, Birhane looks at algorithmic racial bias.
-
Power to the People? Opportunities and Challenges for Participatory AI
Oct. 17, 2022Abeba BirhaneAI fairness, accountability, and transparencyThrough case studies, this paper demonstrates methods for participatory AI.
-
The Values Encoded in Machine Learning Research
June 20, 2022Abeba BirhaneAI bias & discriminationThis paper examimes how and what values are encoded into machine learning.
-
The Forgotten Margins of AI Ethics
June 1, 2022Abeba BirhaneAI bias & discriminationThis paper examines how recent AI ethics literature has addressed topics such as fairness and justice in the context of continued social and structural power asymmetries.
