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Spravedlnost v umělé inteligenci

This module explores fairness in Artificial Intelligence (AI), focusing on identifying and mitigating biases in AI systems. It covers the sources of bias, methods for assessing and improving fairness, and the ethical implications of AI decisions on different populations, aiming to promote equality and prevent discrimination in AI applications.

Tvůrce učebních osnov

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Angwin, Julia, Jeff Larson, Surya Mattu, and Lauren Kirchner. “Machine Bias.” ProPublica, May 23, 2016.

https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Grira, Nizar, and M. Crucianu. “Unsupervised and Semi-Supervised Clustering : A Brief Survey,” 2004.

https://www.semanticscholar.org/paper/Unsupervised-and-Semi-supervised-Clustering-%3A-a-%E2%88%97-Grira-Crucianu/acb799150e4cd3a75fe79ca262047612bd8e5194

Olteanu, Alexandra, Carlos Castillo, Fernando Diaz, and Emre Kıcıman. “Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.” Frontiers in Big Data 2 (July 11, 2019): 13.

https://doi.org/10.3389/fdata.2019.00013

Mehrabi, Ninareh, Fred Morstatter, Nanyun Peng, and Aram Galstyan. “Debiasing Community Detection: The Importance of Lowly-Connected Nodes.” arXiv, 2019.

https://doi.org/10.48550/ARXIV.1903.08136

Fortunato, Santo, and Darko Hric. “Community Detection in Networks: A User Guide.” Physics Reports 659 (2016): 1–44.

https://doi.org/10.1016/j.physrep.2016.09.002

Zhao, Jieyu, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. “Men Also Like Shopping: Reducing Gender Bias Amplification Using Corpus-Level Constraints.” arXiv, 2017.

https://doi.org/10.48550/ARXIV.1707.09457

Mehrabi, Ninareh, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. “A Survey on Bias and Fairness in Machine Learning.” ACM Computing Surveys 54, no. 6 (July 13, 2021): 1–35.

https://doi.org/10.1145/3457607