Research awards

Best Paper Runner-Up Award at WITS, 2021.
Google AI Award for Inclusion Research, 2020.
EECS Rising Stars, 2019.
Best Thematic Paper Award at NAACL, 2019.
Microsoft Research Dissertation Grant, 2018.
1st Place Innovation Award on Data Science at Data for Policy, 2016.
Best Student Presentation Award at Domestic Nuclear Detection Office (DNDO) Academic Research Initiative Grantees Conference, 2015.

Working papers

Leveraging Expert Consistency to Improve Algorithmic Decision Support (with A. Dubrawski, A. Chouldechova)
Best Paper Runner-Up Award at WITS’21.

A case for humans-in-the-loop: decisions in the presence of misestimated algorithmic scores (with R. Fogliato, A. Chouldechova). Extended version of CHI’20.

Social Norm Bias: Residual Harms of Fairness-Aware Algorithms (with M. Cheng, L. Mackey, A. Kalai)
ICML ml4data Workshop Spotlight Talk, ICML Socially Responsible ML Workshop.

On the Relationship Between Explanations, Fairness Perceptions, and Decisions (with J. Schoeffer, N. Kuehl)
ACM CHI 2022 Workshop on Human-Centered Explainable AI (HCXAI).

Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables (with K. Holstein, L. Tumati, Y. Cheng)

More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias (with Y. Li, M. Saar-Tsechansky)

Refereed publications

Note on authorship order: As faculty, I tend to follow Business School convention, which does not give special meaning to the “last author” and orders according to contribution.

Google Scholar Profile

M. De-Arteaga, S. Feuerriegel, M. Saar-Tsechansky. “Algorithmic Fairness in Business Analytics: Directions for Research and Practice”. Production & Operations Management. Forthcoming. [arXiv]

T. Neumann, M. De-Arteaga, S. Fazelpour. “Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms”, In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. [ACM]

S. Fazelpour, M. De-Arteaga. “Diversity in Sociotechnical Machine Learning Systems”. Big Data & Society, 2022. [SAGE].

V. Jeanselme, M. De-Arteaga, J. Elmer, S. M.Perman, A. W. Dubrawski. “Sex differences in post cardiac arrest discharge locations”. Resuscitation Plus, 2021. [Elsevier]

R. Gao, M. Saar-Tsechansky, M. De-Arteaga, M. Lease, M.K. Lee, L. Han. “Human-AI Collaboration with Bandit Feedback”, In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021. [IJCAI]

N. Akpinar, M. De-Arteaga, A. Chouldechova. “The effect of differential victim crime reporting on predictive policing systems”, In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2021. [ACM]

M. De-Arteaga*, R. Fogliato*, and A. Chouldechova. “A case for humans-in-the-loop: decisions in the presence of erroneous algorithmic scores”, In Proceedings of ACM CHI Conference on Human Factors in Computing Systems (CHI), 2020. [ACM, Video, Blog post summary]

R. Fogliato*, M. De-Arteaga*, and A. Chouldechova. “Lessons from the deployment of an algorithmic tool in child welfare”. Fair & Responsible AI Workshop, CHI, 2020. [fair-ai]

A. Romanov, M. De-Arteaga, H. Wallach, J. Chayes, C. Borgs, A. Chouldechova, S. Geyik, K. Kenthapadi, A. Rumshisky, A. Kalai, “What’s in a name? Reducing bias in bios without access to protected attributes”, In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019. [ACL] Best Thematic Paper Award.

M. De-Arteaga, A. Romanov, H. Wallach, J. Chayes, C. Borgs, A. Chouldechova, S. Geyik, K. Kenthapadi, A. Kalai, “Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting”, In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2019. [ACM, GitHub, Video, slides]

N. Swinger*, M. De-Arteaga*, N. Heffernan IV, M. Leiserson, A. Kalai, “What are the biases in my word embedding?”, In Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019. [ACM]

M. De-Arteaga, J. Chen, P. Huggins, J. Elmer, G. Clermont, A. Dubrawski, “Predicting Neurological Recovery with Canonical Autocorrelation Embeddings”, PLoS ONE, 2019. [PLOS, GitHub]

M. De­-Arteaga A. Dubrawski, A. Chouldechova, “Learning under selective labels in the presence of expert consistency”, Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), 2018. [arXiv]

M. De­-Arteaga, W. Herlands, D. Neill, A. Dubrawski, “Machine Learning for the Developing World”, ACM Transactions on Management Information Systems, 2018. [ACM]

M. De­-Arteaga, A. Dubrawski, “Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador”, Data for Policy, 2016. 1st Place Innovation Award on Data Science. [Zenodo]

W. Herlands, M. De­-Arteaga, D. Neill, and A. Dubrawski, “Lass0: Sparse Non­Convex Regression by Local Search”, NIPS Workshop on Optimization for Machine Learning, 2015. [arXiv]

M. De­-Arteaga, I. Eggel, C. Kahn and H. Müller, “Analyzing Image Search Behaviour of Radiologists: Semantics and Prediction of Query Results” Journal of Digital Imaging, 2015. [Springer]

M. De­-Arteaga, I. Eggel, B. Do, D. Rubin, C. Kahn Jr. and H. Müller, “Comparing Image Search Behaviour in the ARRS GoldMiner Search Engine and a Clinical PACS/RIS” Journal of Biomedical Informatics, 2015. [Elsevier]

Other Publications

M. De-Arteaga*, B. Boecking* (2019). “Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism”. arXiv:1906.08206. [arXiv] (*Indicates equal contribution)

M. De­-Arteaga, A. Dubrawski, P. Huggins, “Canonical Autocorrelation Analysis for Radiation Threat Detection”.  Heinz College First Paper / Machine Learning Department Data Analysis Project, 2016. Carnegie Mellon University. [CMU]

A. Riveros, M. De­-Arteaga, F. Gonzalez, S. Jimenez and H. Müller, “MindLab­UNAL: Comparing Metamap and T­-mapper for Medical Concept Extraction in SemEval 2014 Task 7”, International Workshop on Semantic Evaluation (SemEval), 2014. [ACL]

M. De­-Arteaga, S. Jimenez, J. Baquero, G. Dueñas and S. Mancera, “Author Profiling Using Corpus Statistics, Lexicons and Stylistic Features”, PAN Evaluation Lab on Uncovering Plagiarism, Authorship and Social Misuse, CLEF, 2013. [UniWeimar]

M. De­-Arteaga, “Vector models in text mining and its applications to author profiling tasks” (in Spanish), Undergraduate thesis, 2013. [PDF]