298 citations · 502 across the 6 of their papers we have counts for
5 papers · 1 filter
The Use and Misuse of Counterfactuals in Ethical Machine Learning
Atoosa Kasirzadeh, Andrew Smart
The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues…
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…
Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White +6
Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited fo…
Towards a Critical Race Methodology in Algorithmic Fairness
Alex Hanna, Emily Denton, Andrew Smart +1
We examine the way race and racial categories are adopted in algorithmic fairness frameworks. Current methodologies fail to adequately account for the socially constructed nature o…