Responsible AI

This research aims to mitigate algorithmic bias in machine learning through a socio-technical framework applied to algorithmic audits. The project aims to cultivate collaboration between machine learning developers and the broader public, with the objective of minimizing adverse outcomes for all demographic groups arising from the application of machine learning in various decision-making contexts. The project undertakes the task of redefining fairness, acknowledging its inherent contextuality and adaptability, while actively integrating public opinions and attitudes into the process of algorithmic audits. This project positions fairness as a multifaceted phenomenon with social, historical, contextual, and geographical dimensions.

Publications

Jackson, C. B., Ahmad, T., Saxena, D. (2023). Re-imagining Fairness in Machine Learning: A Framework for Building in Socio-cultural and Contextual Awareness. [Position paper presented in the Supporting User Engagement in Testing, Auditing, and Contesting AI workshop]. CSCW. Minneapolis, MN, USA