298 citations · 588 across the 11 of their papers we have counts for
4 papers · 2 filters
Bringing the People Back In: Contesting Benchmark Machine Learning Datasets
Remi Denton, Alex Hanna, Razvan Amironesei +3
In response to algorithmic unfairness embedded in sociotechnical systems, significant attention has been focused on the contents of machine learning datasets which have revealed bi…
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…