Search Results for author: William S. Isaac

Found 4 papers, 0 papers with code

Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context

no code implementations17 Jun 2020 Donald Martin Jr., Vinodkumar Prabhakaran, Jill Kuhlberg, Andrew Smart, William S. Isaac

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 outputs.

BIG-bench Machine Learning Fairness

Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics

no code implementations15 May 2020 Donald Martin Jr., Vinodkumar Prabhakaran, Jill Kuhlberg, Andrew Smart, William S. Isaac

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 impacts on ML system fairness outcomes.

BIG-bench Machine Learning Fairness

A Causal Bayesian Networks Viewpoint on Fairness

no code implementations15 Jul 2019 Silvia Chiappa, William S. Isaac

We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.

Fairness

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