Professorat i Investigació
Directori
De-Arteaga, Maria
Professora titular, Dades, Analytics, Tecnologia i Intel·ligència Artificial a Esade
Professora contractada doctora URL
Formació acadèmica
- Machine Learning & Public Policy. Carnegie Mellon University
- Machine Learning. Carnegie Mellon University
- Matematicas. Universidad Nacional de Colombia
Biografia
La professora De-Arteaga és doctora en Aprenentatge Automàtic i Política Pública per la Carnegie Mellon University, de la qual també és màster en Aprenentatge Automàtic. Abans, va finalitzar els estudis de grau en Matemàtiques a la Universitat Nacional de Colòmbia.
La seva recerca dirà al voltant de l'aprenentatge automàtic centrat en l'home i la intel·ligència artificial, centrant-se en la justícia algorítmica i l'augment de les capacitats humanes amb la IA. El seu treball caracteritza els riscos del biaix algorítmic i proposa nous algorismes i les intervencions sociotècniques per mitigar aquests riscos. Busca entendre com interactuen els humans amb els algorismes i desenvolupar sistemes que promoguin la col·laboració entre els humans i la IA. La seva activitat respon a la creença en la nostra actuació i responsabilitat col·lectiva per configurar les tecnologies que dissenyem o que adoptem.
Reflectint la naturalesa interdisciplinària del seu treball, la seva recerca s'ha publicat en espais destacats de diferents dominis, com revistes com ara Management Science, Production & Operations Management, Data Mining & Knowledge Discovery i Big data & Society, o als principals congressos de ciència computacional, com CHI, IJCAI, WWW, CSCW i FAccT, entre d’altres. La seva recerca ha rebut finançament de diverses organitzacions, com NIH, Microsoft i Google.
Abans d'incorporar-se a Esade, fou professora adjunta del Departament d'Informació, Risc i Direcció d'Operacions de la McCombs School of Business de la Universitat de Texas a Austin. Entre 2022 i 2025, fou membre del Comitè Executiu de la Conference on Fairness, Accountability, and Transparency ACM FAccT.
Publicacions destacades
- Gupta, S., De-Arteaga, M. & Lease, M. (2026). Fairness-Aware Multi-Group Target Detection in Online Discussion. ACM FAccT 2026 - Proceedings of the 9th annual ACM Conference on Fairness, Accountability, and Transparency (pp. 7769-7792). Association for Computing Machinery. DOI: https://doi.org/10.1145/3805689.3806441.
- Mickel, J., De-Arteaga, M., Leqi, L. & Tian, K. (2026). More of the Same: Persistent Representational Harms Under Increased Representation. Advances in Neural Information Processing Systems, 38, pp. 1-39.
- Neumann, T., De-Arteaga, M. & Fazelpour, S. (2026). Should You Use LLMs to Simulate Opinions? Quality Checks for Early-Stage Deliberation. In Koenig, S., Jenkins, C. & Taylor, M. E. (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence (46th ed., pp. 39070-39079). Association for the Advancement of Artificial Intelligence. DOI: https://doi.org/10.1609/aaai.v40i46.41254.
- Schoeffer, J., De-Arteaga, M. & Elmer, J. (2025). Perils of Label Indeterminacy. Proceedings Of The 2025 Acm Conference On Fairness, Accountability, And Transparency, Acm Facct 2025 (pp. 1080-1094). Association for Computing Machinery, Inc. DOI: https://doi.org/10.1145/3715275.3732070.
- Elmer, J., Coppler, P. J., Ratay, C., Steinberg, A., Difiore-Sprouse, S., Case, N., Fischhoff, B., De-Arteaga, M., Cariou, A., Rabinstein, A. A., Rossetti, A. O., Doshi, A. A., Molyneaux, B. J., Dezfulian, C., Maciel, C. B., Leithner, C., Hsu, C. H., Sandroni, C., Greer, D. M., Seder, D. B., Guyette, F. X., ... (2025). Recovery Potential in Patients After Cardiac Arrest Who Die After Limitations or Withdrawal of Life Support. JAMA Network Open, 8 (3), pp. 1-12. DOI: https://doi.org/10.1001/jamanetworkopen.2025.1714.
- Gupta, S., Kovatchev, V., Das, A., De-Arteaga, M. & Lease, M. (2025). Finding Pareto trade-offs in fair and accurate detection of toxic speech. Information Research, 30 (iConf 2025), pp. 123-141. DOI: https://doi.org/10.47989/ir30iConf47572.
- Li, Y., De-Arteaga, M. & Saar-Tsechansky, M. (2024). Label Bias: A Pervasive and Invisibilized Problem. Notices of the American Mathematical Society, 71 (8), pp. 1069-1077. DOI: https://doi.org/10.1090/noti2941.
- Deck, L., Schoeffer, J., De-Arteaga, M. & Kühl, N. (2024). A Critical Survey on Fairness Benefits of Explainable AI. 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024 (pp. 1579-1595). Association for Computing Machinery, Inc. DOI: https://doi.org/10.1145/3630106.3658990.
- Schoeffer, J., De-Arteaga, M. & Kühl, N. (2024). Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making. CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems (pp. 1-18). Association for Computing Machinery. DOI: https://doi.org/10.1145/3613904.3642621.
- Cheng, M., De-Arteaga, M., Mackey, L. & Kalai, A. T. (2023). Social norm bias: residual harms of fairness-aware algorithms. Data Mining and Knowledge Discovery, 37 (5), pp. 1858-1884. DOI: https://doi.org/10.1007/s10618-022-00910-8.
- Gupta, S., Lee, S., De-Arteaga, M. & Lease, M. (2023). Same Same, But Different. ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 (pp. 3689-3700). Association for Computing Machinery, Inc. DOI: https://doi.org/10.1145/3543507.3583290.
- Tahaei, M., Constantinides, M., Quercia, D., Kennedy, S., Muller, M., Stumpf, S., Liao, Q. V., Baeza-Yates, R., Aroyo, L., Holbrook, J., Luger, E., Madaio, M., Blumenfeld, I. G., De-Arteaga, M., Vitak, J. & Olteanu, A. (2023). Human-Centered Responsible Artificial Intelligence. CHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-4). Association for Computing Machinery. DOI: https://doi.org/10.1145/3544549.3583178.
- Holstein, K., De-Arteaga, M., Tumati, L. & Cheng, Y. (2023). Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables. Proceedings of the ACM on Human-Computer Interaction, 7 (CSCW1), pp. 1-20. DOI: https://doi.org/10.1145/3579628.
- Elmer, J., Kurz, M. C., Coppler, P. J., Steinberg, A., Demasi, S., De-Arteaga, M., Simon, N., Zadorozhny, V. I., Flickinger, K. L. & Callaway, C. W. (2023). Time to Awakening and Self-Fulfilling Prophecies after Cardiac Arrest. Critical Care Medicine, 51 (4), pp. 503-512. DOI: https://doi.org/10.1097/CCM.0000000000005790.
- De-Arteaga, M. & Elmer, J. (2023, February). Self-fulfilling prophecies and machine learning in resuscitation science. Resuscitation, 183, 109622. DOI: https://doi.org/10.1016/j.resuscitation.2022.10.014.
- Li, Y., De-Arteaga, M. & Saar-Tsechansky, M. (2022). More Data Can Lead Us Astray. In Hsu, J., Yin, M. (Ed.), HCOMP 2022 - Proceedings of the 10th AAAI Conference on Human Computation and Crowdsourcing (pp. 133-146). Association for the Advancement of Artificial Intelligence AAAI. DOI: https://doi.org/10.1609/hcomp.v10i1.21994.
- De-Arteaga, M., Feuerriegel, S. & Saar-Tsechansky, M. (2022). Algorithmic fairness in business analytics: Directions for research and practice. Production and Operations Management, 31 (10), pp. 3749-3770. DOI: https://doi.org/10.1111/poms.13839.
- Neumann, T., De-Arteaga, M. & Fazelpour, S. (2022). Justice in Misinformation Detection Systems. FAccT 2022 (pp. 1504-1515). Association for Computing Machinery. DOI: https://doi.org/10.1145/3531146.3533205.
- Jeanselme, V., De-Arteaga, M., Zhang, Z., Barrett, J. & Tom, B. (2022). Imputation strategies under clinical presence: Impact on algorithmic fairness. Proceedings of Machine Learning Research, 193, pp. 12-34.
- Fazelpour, S. & De-Arteaga, M. (2022). Diversity in sociotechnical machine learning systems. Big Data and Society, 9 (1), pp. 1-14. DOI: https://doi.org/10.1177/20539517221082027.
- Jeanselme, V., De-Arteaga, M., Elmer, J., Perman, S. M. & Dubrawski, A. (2021). Sex differences in post cardiac arrest discharge locations. Resuscitation Plus, 8, pp. 1-5. DOI: https://doi.org/10.1016/j.resplu.2021.100185.
- Gao, R., Saar-Tsechansky, M., De-Arteaga, M., Han, L., Lee, M. K. & Lease, M. (2021). Human-AI Collaboration with Bandit Feedback. In Zhou, Z. (Ed.), Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 (pp. 1722-1728). International Joint Conferences on Artificial Intelligence. DOI: https://doi.org/10.24963/ijcai.2021/237.
- Akpinar, N. J., De-Arteaga, M. & Chouldechova, A. (2021). The effect of differential victim crime reporting on predictive policing systems. FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 838-849). Association for Computing Machinery, Inc. DOI: https://doi.org/10.1145/3442188.3445877.
- De-Arteaga, M., Fogliato, R. & Chouldechova, A. (2020). A Case for Humans-in-the-Loop. CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-12). Association for Computing Machinery. DOI: https://doi.org/10.1145/3313831.3376638.
- De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K. & Kalai, A. T. (2019). Bias in BIOS. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (pp. 120-128). Association for Computing Machinery, Inc. DOI: https://doi.org/10.1145/3287560.3287572.
- Swinger, N., De-Arteaga, M., Thomas Heffernan, N., Leiserson, M. D. & Kalai, A. T. (2019). What are the biases in my word embedding?. AIES 2019 - Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 305-311). Association for Computing Machinery, Inc. DOI: https://doi.org/10.1145/3306618.3314270.
- De-Arteaga, M., Chen, J., Huggins, P., Elmer, J., Clermont, G. & Dubrawski, A. (2019). Predicting neurological recovery with Canonical Autocorrelation Embeddings. PLOS ONE, 14 (1), pp. 1-17. DOI: https://doi.org/10.1371/journal.pone.0210966.
- Romanov, A., De-Arteaga, M., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., Rumshisky, A. & Kalai, A. T. (2019). What's in a name? Reducing bias in BIOS without access to protected attributes. Long and Short Papers (pp. 4187-4195). Association for Computational Linguistics ACL.
- De-Arteaga, M., Herlands, W., Neill, D. B. & Dubrawski, A. (2018). Machine learning for the developing world. ACM Transactions on Management Information Systems, 9 (2), 9. DOI: https://doi.org/10.1145/3210548.
- De-Arteaga, M., Eggel, I., Kahn, C. E. & Müller, H. (2015). Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results. Journal of Digital Imaging, 28 (5), pp. 537-546. DOI: https://doi.org/10.1007/s10278-015-9792-6.
- De-Arteaga, M., Eggel, I., Do, B., Rubin, D., Kahn, C. E. & Müller, H. (2015). Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS. Journal of Biomedical Informatics, 56, pp. 57-64. DOI: https://doi.org/10.1016/j.jbi.2015.04.013.
- Riveros, A., De-Arteaga, M., González, F. A., Jimenez, S. & Müller, H. (2014). MindLab-UNAL. In Nakov, P., Zesch, T. (Ed.), 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings (pp. 424-427). Association for Computational Linguistics ACL. DOI: https://doi.org/10.3115/v1/s14-2073.
- De-Arteaga, M., Jimenez, S., Dueñas, G., Mancera, S. & Baquero, J. (2013). Author profiling using corpus statistics, lexicons and stylistic features: Notebook for PAN at CLEF-2013. CEUR Workshop Proceedings, 1179.