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.
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]
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]
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 (FAT*), 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 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]
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]
“Will machine learning improve or bias expert decision-making?” Software Engineering Institute (CMU), 2019.
“Inteligencia Artificial Responsable”, Feria Digitech (Bogota, Colombia), 2019.
“What are the biases in my data and why do they matter?” IBM Research Africa, 2019.
“Bias in bios: fairness in a high-stakes machine-learning setting”, Invited lecture at Computational Ethics in NLP (CMU), 2019.
“Guiding public service agencies with machine learning: Opportunities and risks”, AMS Sectional Meeting, Special Session on Social Change In and Through Mathematics and Education, 2018.
“Fairness In Prediction Models Used By Public Service Agencies”, INFORMS, 2018.
“Challenges of data-driven decision making with humans in the loop”, Google Fairness in ML Workshop, 2018.
“Machine learning, sexual violence crimes and decision-support systems”, Office of the Attorney General of Colombia, 2018.
“Learning under selective labels in the presence of expert consistency”, Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), 2018.
“Machine Learning for the Developing World” Data science and machine learning for development and humanitarian action session, UNESCO Tech4Dev Conference, 2018. Closing Keynote.
“Machine Learning for the Developing World”, École polytechnique fédérale de Lausanne (EPFL), 2018.
“Using expert consensus to learn under selective labels”, Google Women in Tech Summit, 2018.
“Challenges of Child Maltreatment Prediction Models”, Allegheny County Department of Human Services, 2017.
“Leveraging Multidimensional Autocorrelations to Boost Sensitivity of Spectral Anomaly Detection”, DNDO Annual Academic Research Initiative Grantees Conference, 2015. Best Student Presentation Award.
“Author Profiling, an Application of Computational Linguistics”, Colombian Congress of Young Linguists, 2013.