Biases and debiasing in human and artificial intelligence

Patrizia Catellani; Marco Piastra

doi:10.35295/osls.iisl.2304

Authors

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

DOI:

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

Keywords:

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

Abstract

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.

Downloads

Download data is not yet available.

Metrics

Global Statistics

50 Views	26 Downloads
76 Total

Author Biographies

Professor 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

Dr. 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.

References

Alicke, M.D., et al., 2008. Culpable control and counterfactual reasoning in the psychology of blame. Personality and Social Psychology Bulletin [online], 34(10), 1371–1381. Available at: https://doi.org/10.1177/0146167208321594 DOI: https://doi.org/10.1177/0146167208321594

Angwin, J., and Larson, J., 2015. Machine Bias. ProPublica [online], 23 May. Available at: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

Bickel, P.J., Hammel, E.A., and O’Connell, J.W., 1975. Sex Bias in Graduate Admissions: Data from Berkeley. Science [online], 187(4175), 398-404. Available at: https://doi.org/10.1126/science.187.4175.398 DOI: https://doi.org/10.1126/science.187.4175.398

Bishop, C. M., and Bishop, H., 2023. Deep Learning: Foundations and Concepts [online]. Cham: Springer Nature. Available at: https://doi.org/10.1007/978-3-031-45468-4 DOI: https://doi.org/10.1007/978-3-031-45468-4

Catellani, P., 2010. Fatti e controfatti nel ragionamento giudiziario. Sistemi Intelligenti, 22(3), 209–222. Available at: https://www.rivisteweb.it/doi/10.1422/32620

Chiappa, S., 2019. Path-Specific Counterfactual Fairness. Proceedings of the AAAI Conference on Artificial Intelligence [online], 33(01). Available at: https://doi.org/10.1609/aaai.v33i01.33017801 DOI: https://doi.org/10.1609/aaai.v33i01.33017801

Corbett-Davies, S., et al., 2016. A computer program used for bail and sentencing decisions was labeled biased against blacks. It’s actually not that clear. The Washington Post, 17 October.

Dale, S., 2015. Heuristics and biases: The science of decision-making. Business Information Review [online], 32(2), 93–99. Available at: https://doi.org/10.1177/0266382115592536 DOI: https://doi.org/10.1177/0266382115592536

Damasio, A.R., 1996. The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences [online], 351(1846), 1413–1420. Available at: https://doi.org/10.1098/rstb.1996.0125 DOI: https://doi.org/10.1098/rstb.1996.0125

Danziger, S., Levav, J., and Avnaim–Pesso, L., 2011. Extraneous factors in judicial decisions. Proceedings of the National Academy of Sciences [online], 108(17), 6889–6892. Available at: https://doi.org/10.1073/pnas.1018033108 DOI: https://doi.org/10.1073/pnas.1018033108

Dressel, J., and Farid, H., 2016. The accuracy, fairness, and limits of predicting recidivism. Science Advances [online], (4)1, January. Available at: https://doi.org/10.1126/sciadv.aao5580 DOI: https://doi.org/10.1126/sciadv.aao5580

Gawronski, B., et al., 2018. Effects of incidental emotions on moral dilemma judgments: An analysis using the CNI model. Emotion [online], 18(7), 989–1008. Available at: https://doi.org/10.1037/emo0000399 DOI: https://doi.org/10.1037/emo0000399

Gigerenzer, G., 2008. Gut feelings. Short cuts to better decision making. London: Penguin.

Hertwig, R., and Grüne–Yanoff, T., 2017. Nudging and boosting: Steering or empowering good decisions. Perspectives on Psychological Science [online], 12(5), 973–986. Available at: https://doi.org/10.1177/1745691617702496 DOI: https://doi.org/10.1177/1745691617702496

Inzlicht, M., Legault, L., and Teper, R., 2014. Exploring the mechanisms of self–control improvement. Current Directions in Psychological Science [online], 23(4), 302–307. Available at: https://doi.org/10.1177/0963721414534256 DOI: https://doi.org/10.1177/0963721414534256

Kahneman, D., 2011. Thinking, fast and slow. New York: FSG Adult.

Kirkpatrick, K., 2017. It’s not the algorithm, it’s the data. Communications of the ACM [online], 60(2). Available at: https://doi.org/10.1145/3022181 DOI: https://doi.org/10.1145/3022181

MacDougall, C., and Baum, F., 1997. The devil’s advocate: A strategy to avoid groupthink and stimulate discussion in focus groups. Qualitative Health Research [online], 7(4), 532–541. Available at: https://doi.org/10.1177/104973239700700407 DOI: https://doi.org/10.1177/104973239700700407

Martin, J.W., and Cushman, F., 2016. Why we forgive what can’t be controlled. Cognition [online], 147(4), 133–148. Available at: https://doi.org/10.1016/j.cognition.2015.11.008 DOI: https://doi.org/10.1016/j.cognition.2015.11.008

McClure, J., Hilton, D.J., and Sutton, R.M., 2007. Judgments of voluntary and physical causes in causal chains: Probabilistic and social functionalist criteria for attributions. European Journal of Social Psychology [online], 37(6), 879–901. Available at: https://doi.org/10.1002/ejsp.394 DOI: https://doi.org/10.1002/ejsp.394

Metcalfe, J., and Shimamura, A.P., 1994. Metacognition: Knowing about knowing [online]. Cambridge, MA: MIT Press. Available at: https://doi.org/10.7551/mitpress/4561.001.0001 DOI: https://doi.org/10.7551/mitpress/4561.001.0001

Pearl, J., Glymour, M., and Jewell, N.P., 2016. Causal Inference in Statistics: A Primer. Hoboken: Wiley.

Roese, N.J., and Olson, J.M., 1996. Counterfactuals, causal attributions, and the hindsight bias: A conceptual integration. Journal of Experimental Social Psychology [online], 32(3), 197–227. Available at: https://doi.org/10.1006/jesp.1996.0010 DOI: https://doi.org/10.1006/jesp.1996.0010

Savioni, L., and Triberti, S., 2020. Cognitive biases in chronic illness and their impact on patients’ commitment. Frontiers in Psychology [online], 11, 579455. Available at: https://doi.org/10.3389/fpsyg.2020.579455 DOI: https://doi.org/10.3389/fpsyg.2020.579455

Smith, C., and Greene, E., 2005. Conduct and its consequences: Attempts at debiasing jury judgments. Law and Human Behavior [online], 29(5), 505–526. Available at: https://doi.org/10.1007/s10979-005-5692-5 DOI: https://doi.org/10.1007/s10979-005-5692-5

Smith, M., 2016. In Wisconsin, a Backlash Against Using Data to Foretell Defendants’ Futures. The New York Times, 22 June.