29 citations · 60 across the 4 of their papers we have counts for
5 papers
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…
Social Biases in NLP Models as Barriers for Persons with Disabilities
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton +3
Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in…
Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Vinodkumar Prabhakaran, Ben Hutchinson, Margaret Mitchell
Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language. However, most language da…
Dialog Structure Through the Lens of Gender, Gender Environment, and Power
Vinodkumar Prabhakaran, Owen Rambow
Understanding how the social context of an interaction affects our dialog behavior is of great interest to social scientists who study human behavior, as well as to computer scient…