Ahmed Abbasi

Ahmed Abbasi’s research examines socio-technical phenomena at the intersection of people and machine learning (ML) models and systems. Most of his research examines the design, deployment, and governance implications of predictive ML in contexts involving human-centered patterns, such as user behavior modeling or natural language processing (NLP). His recent work has focused on fairness and bias in predictive analytics, including how to design fair ML, the limitations of current fairness metrics and debiasing strategies, and the role of literacy and numeracy in understanding the efficacy and implications of artificial intelligence systems that rely heavily on ML. His work also explores the importance of domain-adaptation and human-involvement in contexts involving large-scale ML models and AutoML.

Abbasi directs the Analytics Ph.D. program at the Mendoza College of Business and also serves as co-director for the Human-centered Analytics Lab (HAL). He received his Ph.D. in information systems from the Artificial Intelligence Lab at the University of Arizona.