MISC talk: Beyond Bias: Algorithmic Unfairness, Infrastructure, and Genealogies of Data 

Abstract:

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 crowdwork and data mining. In this talk, I 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. These genealogies should be attentive to the constellation of organizations and stakeholders involved in their creation, the intent, values, and assumptions of their authors and curators, and the adoption of datasets by subsequent researchers.

Speaker Bio: 

Alex Hanna is a sociologist and research scientist working on machine learning fairness and ethical AI at Google. Before that, she was an Assistant Professor at the Institute of Communication, Culture, Information and Technology at the University of Toronto. Her research centers on origins of the training data which form the informational infrastructure of AI and algorithmic fairness frameworks, and the way these datasets exacerbate racial, gender, and class inequality. 

The link and passcode for the Zoom meeting are available on the MISC website calendar.

About MISC:

Michigan Interactive and Social Computing (http://misc.si.umich.edu/) connects researchers studying human-computer interaction, social computing, technology design, and computer-supported cooperative work across the University of Michigan.