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Maria De-Arteaga

Assistant Professor

Department:     Information, Risk & Operations Management

Research Areas:     Artificial Intelligence

Maria DeArteaga headshot

Maria De-Arteaga is an assistant professor at The University of Texas at Austin’s McCombs School of Business. She is also a core member of the Machine Learning Laboratory and a Good Systems researcher. She has taught courses on programming and AI ethics, and she serves as a Ph.D. adviser for students in the IROM Ph.D. Program and in other departments across campus, such as Computer Science.

De-Arteaga’s academic research focuses on algorithmic fairness and human-AI complementarity. She seeks to characterize and mitigate risk of algorithmic biases, and to develop algorithms and sociotechnical interventions to improve AI-supported decision making.

Her work has been published in top computer science conferences such as ACM Conference on Human Factors in Computing Systems (CHI), ACM Web Conference (WWW), International Joint Conference on Artificial Intelligence (IJCAI), ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), and ACM Conference on Fairness, Accountability, and Transparency (FAccT). Her research is in top journals across domains, including Data Mining and Knowledge Discovery, Production and Operations Management, Critical Care Medicine, Resuscitation, and Big Data and Society. She has been consulted as an expert by CNN, The New York Times, and The Wall Street Journal to discuss bias in artificial intelligence, and she has given keynotes at Harvard HDSI Annual Conference, MIT Lincoln Laboratory, Facebook, the International Conference on AI for People (CAIP), a UNESCO Tech4Dev Conference session, and the Colombian Mathematical Congress.

Prior to joining McCombs, De-Arteaga worked in investigative journalism. She has written pieces on the threats faced by Colombian social leaders, sexual violence in El Salvador, and algorithms and crime.

De-Arteaga earned a B.S. in mathematics from Universidad Nacional de Colombia and an M.S. in machine learning from Carnegie Mellon University, where she also earned a joint Ph.D. in machine learning and public policy.



Honorable Mention Award, ACM Conference on Human Factors in Computing Systems (CHI)


Honorable Mention Award at ACM Conference on Human Factors in Computing Systems (CHI)


NIH R01 Grant


Best Student Paper Award (student author: Ruijiang Gao) at Conference on Information Systems and Technology


Best Paper Runner-Up Award at the Workshop on Information Technologies and Systems


UT Austin Machine Learning Laboratory Grant

Jakob Schoeffer, Maria De-Arteaga, and Niklas, Kuhl.  May 2024.
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making.
Proceedings of the CHI Conference on Human Factors in Computing Systems, Article 836, pp. 1-18.

Soumyajit Gupta, Sooyong Lee, Maria De-Arteaga, and Matthew Lease. 2023. Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection. Proceedings of the ACM Web Conference, WWW 2023.

Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati, and Yanghuidi Cheng. April 2023. Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables.  Proceedings of the ACM on Human-Computer Interaction 7(CSCW1):1-20.

Jonathan Elmer, Michael Kurz, Patrick Coppler, Alexis Steinberg, Stephanie DeMasi, Maria De-Arteaga, Noah Simon, Vladimir Zadorozhny, Katharyn Flickinger, and Clifton Callaway Time to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest. Critical Care Medicine.  Forthcoming.

Myra Cheng, Maria De-Arteaga, Lester Mackey, and Adam T. Kalai. Social Norm Bias: Residual Harms of Fairness-Aware Algorithms. Data Mining and Knowledge Discovery. Forthcoming.

Yunyi Li, Maria De-Arteaga, and Maytal Saar-Tsechansky. When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing 10(1):133-146.

Maria De-Arteaga and Jonathan Elmer. Self-fulfilling Prophecies and Machine Learning in Resuscitation Science. Resuscitation.  Forthcoming.

Maria De‐Arteaga, Stefan Feuerriegel, and Maytal Saar‐Tsechansky. Oct 2022. Algorithmic Fairness in Business Analytics: Directions for Research and Practice. Production & Operations Management 31(10): 3749-3770.

Maria De-Arteaga, Sina Fazelpour. Diversity in Sociotechnical Machine Learning Systems. Big Data & Society. Forthcoming.

Vincent Jeanselme, Maria De Arteaga Gonzalez, Jonathan Elmer, Sarah M. Perman, and Artur Dubrawski. Gender Differences in Post Cardiac Arrest Discharge Locations. Resuscitation Plus, forthcoming.