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Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods.

, , , , , , , , , , , and . Big Data Cogn. Comput., 7 (1): 15 (March 2023)

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Learning and Mining Player Motion Profiles in Physically Interactive Robogames., , , , and . Future Internet, 10 (3): 22 (2018)Bias and unfairness in machine learning models: a systematic literature review., , , , , , , , , and 2 other author(s). CoRR, (2022)Robot player adaptation to human opponents in physical, competitive robogames., , and . RO-MAN, page 851-856. IEEE, (2020)Activity recognition in a physical interactive robogame., , , and . ICDL-EPIROB, page 92-97. IEEE, (2017)Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods., , , , , , , , , and 2 other author(s). Big Data Cogn. Comput., 7 (1): 15 (March 2023)Context-Based Patterns in Machine Learning Bias and Fairness Metrics: A Sensitive Attributes-Based Approach., , , , , , , , and . Big Data Cogn. Comput., 7 (1): 27 (March 2023)LEARNING MODELS TO OPTIMIZE THE PLAYER EXPERIENCE IN ROBOGAMES.. Polytechnic University of Milan, Italy, (2019)Modeling Player Activity in a Physical Interactive Robot Game Scenario., , , and . HAI, page 411-414. ACM, (2017)Deceptive Actions to Improve the Attribution of Rationality to Playing Robotic Agents., , , and . Int. J. Soc. Robotics, 13 (2): 391-405 (2021)