Umang Bhatt

Papers

Total Citations

188

H-Index

About

Umang Bhatt is a leading researcher at the intersection of artificial intelligence, human-computer interaction, and algorithmic fairness. Their work focuses on understanding how transparency, interpretability, and trust shape the deployment of AI systems in real-world contexts. Bhatt’s most-cited paper, “How transparency modulates trust in artificial intelligence” (2022, 188 citations), provides foundational insights into the delicate balance between explainability and user reliance in human-AI teams. This work bridges behavioral science and machine learning, offering empirical evidence that transparency can both build and undermine trust depending on context. Bhatt has also made significant contributions to the development of practical fairness toolkits and auditing frameworks for machine learning pipelines, helping to operationalize ethical AI in industry and policy settings. Their research is widely cited across computer science, engineering, and management disciplines, reflecting its interdisciplinary impact. Bhatt’s work has been recognized by major conferences and workshops in AI ethics and interpretability, and they are known for advancing rigorous, human-centered approaches to responsible AI. For students and researchers, Bhatt’s scholarship offers a critical lens on how to design AI systems that are not only accurate but also trustworthy and just.