This work bridges computational fairness, causal interpretability, and Responsible AI communication. We create interactive tools that let users visualize, question, and reconfigure both data-driven models and the social narratives they inform. Since bias and fairness are inherently subjective and mean different things to different people, the systems developed in this research feature well-designed interactive visual interfaces that put the human in the loop—empowering users to take an active role in understanding and mitigating bias, misinformation, and misconceptions that arise in algorithmic decisions, causal reasoning, visual communication, and public discourse on polarizing issues such as climate change, health, and social policy. Increasingly, these systems also leverage large language models to simulate diverse personas, enabling the study of how reasoning, messaging, and belief formation differ across social, psychological, and cultural contexts.

	Leveraging Large Language Models for Personalized Public Messaging A. Kumar Das, C. Xiong Bearfield, K. Mueller ACM CHI Conference on Human Factors in Computing Systems Late Breaking Work Tokyo, Japan, May 2025.
	FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness T. Behzad, M. Singh, A. Ripa, K. Mueller Proceedings of the ACM on Human-Computer Interaction (CSCW) 9(2):1-30, 2025
	Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations S. Salim, N. Hoque, K. Mueller Proceedings of the ACM on Human-Computer Interaction, 8 (CSCW 1) 1-37, 2024
	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