Biases and debiasing in human and artificial intelligence

Patrizia Catellani; Marco Piastra

doi:10.35295/osls.iisl.2304

Autores/as

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

Palabras clave:

sesgo, eliminación de sesgo, razonamiento causal, atribución de responsabilidad, inteligencia artificial

Resumen

Cuando tomamos decisiones o discutimos, los factores cognitivos, emocionales y motivacionales a menudo nos llevan a utilizar atajos mentales. Estos pueden acelerar el razonamiento, pero también pueden dar lugar a sesgos sistemáticos. Para contrarrestarlos, se pueden probar experimentalmente diversas estrategias de eliminación de sesgos. En el sistema judicial, es fundamental que los procedimientos de toma de decisiones se ajusten a las capacidades y límites de la cognición humana. La inteligencia artificial también se ve afectada por sesgos, pero estos no se deben a la subjetividad de los algoritmos, sino a fallos en los métodos formales. El análisis metodológico riguroso es esencial para mejorar la concienciación y el control. Los algoritmos pueden ajustarse y supervisarse para garantizar que sus predicciones y acciones reflejen la realidad y sirvan a los objetivos previstos. Tanto en la inteligencia humana como en la artificial, las estrategias para eliminar sesgos ayudan a reducir los sesgos y a mejorar la calidad del razonamiento y las decisiones.

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Biografía del autor/a

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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