MMLLAEIML
3
24 HORAS
Al finalizar el curso, los estudiantes serán capaces de analizar, interpretar, evaluar y diseñar sistemas de aprendizaje automático que integren principios de equidad, utilizando herramientas de auditoría, métricas de equidad, métodos de interpretabilidad y técnicas de mitigación de sesgos en modelos de clasificación y modelos generativos de lenguaje.
Introducción a la equidad algorítmica
Modelos de clasificación y evaluación de sesgos
Interpretabilidad y explicabilidad en Machine Learning
Mitigación del sesgo en clasificación
Modelos generativos de lenguaje (LLMs) y sesgo
Barrocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT Press. https://fairmlbook.org/index.html
Molnar, C. (2025). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. https://christophm.github.io/interpretable-ml-book/
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), pp. 1-35. ACM. https://doi.org/10.1145/3457607
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the 2019 ACM Conference on Fairness, Accountability, and Transparency (pp. 220–229). ACM. https://doi.org/10.1145/3287560.3287596
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. In Nature Machine Intelligence 1 (pp. 206-215). https://doi.org/10.1038/s42256-019-0048-x
Barrocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT Press. https://fairmlbook.org/index.html
Molnar, C. (2025). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. https://christophm.github.io/interpretable-ml-book/
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), pp. 1-35. ACM. https://doi.org/10.1145/3457607
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the 2019 ACM Conference on Fairness, Accountability, and Transparency (pp. 220–229). ACM. https://doi.org/10.1145/3287560.3287596
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. In Nature Machine Intelligence 1 (pp. 206-215). https://doi.org/10.1038/s42256-019-0048-x
Andrés Abeliuk
Beauchef #851, Edificio Poniente, segundo piso, oficinas 210-214. Santiago
Departamento de Ciencias de la Computación
Facultad de Ciencias Físicas y Matemáticas
Universidad de Chile
Beauchef 851, Edificio Norte, Tercer Piso
contacto@dcc.uchile.cl
Santiago, Chile. C.P. 837-0456
+562 2978 0652
contacto@dcc.uchile.cl
