29 citations · 48 across the 4 of their papers we have counts for
4 papers
Towards Geo-Culturally Grounded LLM Generations
Piyawat Lertvittayakumjorn, David Kinney, Vinodkumar Prabhakaran +2
Generative large language models (LLMs) have demonstrated gaps in diverse cultural awareness across the globe. We investigate the effect of retrieval augmented generation and searc…
Advancing Community Engaged Approaches to Identifying Structural Drivers of Racial Bias in Health Diagnostic Algorithms
Jill A. Kuhlberg, Irene Headen, Ellis A. Ballard +1
Much attention and concern has been raised recently about bias and the use of machine learning algorithms in healthcare, especially as it relates to perpetuating racial discriminat…
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…