Beyond Bias: Algorithmic Unfairness, Infrastructure and Genealogies of Data | Alex Hanna | WiDS 2022
About This Video
Alex Hanna, Director of Research, DAIR Institute, presents a Technical Vision Talk at the WiDS Worldwide conference.
Problems of algorithmic bias are often framed in terms of lack of representative data or formal fairness optimization constraints to be applied to automated decision-making systems. However, these discussions sidestep deeper issues with data used in AI, including problematic categorizations and the extractive logics of crowd work and data mining.
In this talk Alex will make two interventions: first by reframing of data as a form of infrastructure, and as such, implicating politics and power in the construction of datasets; and secondly discussing the development of a research program around the genealogy of datasets used in machine learning and AI systems.
In This Video
Director of Research, The Distributed AI Research Institute (DAIR)
Social scientist with experience in politics, natural language processing, and fairness in machine learning and AI.