In this line of work we develop visual interfaces that help users identify and correct biases in algorithmic decision making systems. We typically use causal models and do so in two ways: (1) as explainable models that encode black box models, such as neural networks, and give insights into how certain decisions come about, and (2) as interpretable models that can operate as fully functional and tunable algorithmic decision systems. Since bias and fairness are quite subjective and mean different things to different people, the systems developed in this research typically have well developed interactive visual interfaces that put the human in the loop to take on an active role in the bias mitigation process.

	D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias B. Ghai, K. Mueller IEEE Trans. on Visualization and Computer Graphics (Special Issue IEEE VIS 2022) 29(1):473-482, 2023
	Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions B. Ghai, M. Mishra, K. Mueller ACM Conference on Information and Knowledge Management (CIKM) Atlanta, GA, October, 2022
	WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings B. Ghai, Md. N. Hoque, K. Mueller ACM CHI Late Breaking Work Virtual, May 8-13, 2021
	Measuring Social Biases of Crowd Workers using Counterfactual Queries B Ghai, QV Liao, Y Zhang, K Mueller Fair & Responsible AI Workshop (co-located with CHI 2020) Virtual, April 2020
	Toward Interactively Balancing the Screen Time of Actors Based on Observable Phenotypic Traits in Live Telecast Md. N Hoque, S. Billah, N. Saquib, K. Mueller ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) Virtual, October 17-21, 2020