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Bias mitigation for automated decision making systems

Alice Morano

Bias mitigation for automated decision making systems.

Rel. Antonio Vetro', Elena Beretta. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020

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The increasingly widespread use of data, including personal data or relating to sensitive information, has inevitably contributed to the increase in social discrimination especially concerning automated decision systems, such as ranking systems. Previous research has made progress in identifying various types of bias that can lead to discrimination in machine learning. Techniques have been developed to reduce bias, but there are limitations in the scope of application: it is not possible to completely reset the present bias, as it is not possible to simultaneously satisfy all the principles of fairness in machine learning. We used three known datasets in the ML fairness field: the COMPAS dataset, a credit scoring dataset and a dataset with information related to drug use. Based on these data, we focused on the study of the fairness metrics and how we could improve fairness by applying certain bias mitigation techniques. Our results confirm the findings of previous research and highlight a possible process to maximize fairness, through a combined use of bias mitigation techniques.

Relators: Antonio Vetro', Elena Beretta
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 172
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Ente in cotutela: Kyoto Institute of Technology (GIAPPONE)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/16657
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