Biases and debiasing in human and artificial intelligence

Patrizia Catellani; Marco Piastra

doi:10.35295/osls.iisl.2304

Autores

Patrizia Catellani Catholic University o the Sacred Heart, Milan https://orcid.org/0000-0002-7195-8967
Marco Piastra University of Pavia https://orcid.org/0000-0003-2556-5254

DOI:

https://doi.org/10.35295/osls.iisl.2304

Palavras-chave:

bias, debiasing, causal reasoning, responsibility attribution, artificial intelligence

Resumo

When we make decisions or argue, cognitive, emotional, and motivational factors often lead us to use mental shortcuts. These can speed up reasoning but can also lead to systematic biases. To counteract them, various debiasing strategies can be tested experimentally. In the justice system, it is crucial that the decision-making procedures match the capabilities and limits of human cognition. Artificial intelligence is also affected by biases, but these are not due to the subjectivity of the algorithms, but to flaws in the formal methods. Rigorous methodological analysis is essential to improve awareness and control. Algorithms can be adjusted and monitored to ensure that their predictions and actions reflect reality and serve their intended goals. Whether in human or artificial intelligence, debiasing strategies help to reduce bias and improve the quality of reasoning and decisions.

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Biografia do Autor

Patrizia Catellani, Catholic University o the Sacred Heart, Milan

Patrizia Catellani is full professor of social psychology at the Catholic University of Milan and director of the research center PsyLab (Psychology, Law and Policy Lab). Her research focuses on reasoning, decision-making and the impact of communication in various areas of social, political and public relevance. She is particularly concerned with how to use new technologies and artificial intelligence algorithms to promote behaviors consistent with health, well-being and environmental sustainability at scale. She is the author of about one hundred and thirty international and national publications, including journal articles, book chapters and volumes. The detailed profile and all activities can be found at www.patriziacatellani.com

Marco Piastra, University of Pavia

Marco Piastra, Ph.D., is an engineer and educator at the University of Pavia, where he teaches graduate courses in Artificial Intelligence and Deep Learning. His research interests include the application of modeling methods, causal models, and deep reinforcement learning to develop advanced strategies across interdisciplinary fields, such as the psychology of human interactions.

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