708 citations · 1.2k across the 14 of their papers we have counts for
6 papers · 1 filter
Cultural Incongruencies in Artificial Intelligence
Vinodkumar Prabhakaran, Rida Qadri, Ben Hutchinson
Artificial intelligence (AI) systems attempt to imitate human behavior. How well they do this imitation is often used to assess their utility and to attribute human-like (or artifi…
Power to the People? Opportunities and Challenges for Participatory AI
Abeba Birhane, William Isaac, Vinodkumar Prabhakaran +4
Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with the view that participation opens…
Re-imagining Algorithmic Fairness in India and Beyond
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson +2
Conventional algorithmic fairness is West-centric, as seen in its sub-groups, values, and methods. In this paper, we de-center algorithmic fairness and analyse AI power in India. B…
Non-portability of Algorithmic Fairness in India
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson +1
Conventional algorithmic fairness is Western in its sub-groups, values, and optimizations. In this paper, we ask how portable the assumptions of this largely Western take on algori…
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context
Donald Martin, Vinodkumar Prabhakaran, Jill Kuhlberg +2
Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and output…
Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics
Donald Martin, Vinodkumar Prabhakaran, Jill Kuhlberg +2
Recent research on algorithmic fairness has highlighted that the problem formulation phase of ML system development can be a key source of bias that has significant downstream impa…