Profesorado e Investigación
Directorio
De-Arteaga, Maria
Profesora titular, Datos, Analytics, Tecnología e Inteligencia Artificial en Esade
Profesora contratada doctora URL
Formación académica
- Machine Learning & Public Policy. Carnegie Mellon University
- Machine Learning. Carnegie Mellon University
- Matematicas. Universidad Nacional de Colombia
Biografía
La profesora De-Arteaga es doctora en Aprendizaje Automático y Política Pública por la Carnegie Mellon University, de la cual también es máster en Aprendizaje Automático. Previamente, finalizó sus estudios de grado en Matemáticas en la Universidad Nacional de Colombia.
Su investigación se centra en el aprendizaje automático centrado en el hombre y la inteligencia artificial, con particular énfasis en la justicia algorítmica y el aumento de las capacidades humanas con la IA. Su trabajo caracteriza los riesgos del sesgo algorítmico y propone nuevos algoritmos y las intervenciones sociotécnicas para mitigar dichos riesgos. Busca entender cómo interactúan los humanos con los algoritmos y desarrollar sistemas que promuevan la colaboración entre los humanos y la IA. Su actividad responde a la fe en nuestra actuación y responsabilidad colectiva para configurar las tecnologías que diseñamos o adoptamos.
Reflejando la naturaleza interdisciplinaria de su trabajo, su investigación se ha publicado en espacios destacados de distintos dominios, como revistas como Management Science, Production & Operations Management, Data Mining & Knowledge Discovery y Big Data & Society, o en los principales congresos de la ciencia computacional como CHI, IJCAI, WWW, CSCW y FAccT, entre otros. Su investigación ha recibido financiación de varias organizaciones, como NIH, Microsoft y Google.
Antes de incorporarse a Esade, fue profesora adjunta del Departamento de Información, Riesgo y Dirección de Operaciones de la McCombs School of Business de la Universidad de Texas en Austin. Entre 2022 y 2025, fue miembro del Comité Ejecutivo de la Conference on Fairness, Accountability, and Transparency ACM FAccT.
Publicaciones destacadas
- 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.
- 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.
- Mickel, J., De-Arteaga, M., Leqi, L. & Tian, K. (2025). More of the Same. 39th Conference on Neural Information Processing Systems, NeurIPS 2025 (pp. 68136-68174). Neural information processing systems foundation.
- 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.
Datos de contacto
Tel: +34 932 806 162
Ext. 2783
Fax: +34 932 048 105
Ext. 2783
Fax: +34 932 048 105