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Article

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

1
Department of Engineering Management, University of Antwerp, 2000 Antwerp, Belgium
2
Columbia Business & Engineering Schools, New York, NY 10027, USA
3
Department of Management, Columbia Business School, New York, NY 10027, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriele Gianini and Pierre-Edouard Portier
Information 2021, 12(12), 518; https://doi.org/10.3390/info12120518
Received: 29 October 2021 / Revised: 3 December 2021 / Accepted: 7 December 2021 / Published: 13 December 2021
(This article belongs to the Special Issue Advances in Explainable Artificial Intelligence)
Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and there exists a positive link between the model’s prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world. View Full-Text
Keywords: psychological profiling; predictive modeling; behavioral data; explainable artificial intelligence; rule extraction; counterfactual explanations psychological profiling; predictive modeling; behavioral data; explainable artificial intelligence; rule extraction; counterfactual explanations
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MDPI and ACS Style

Ramon, Y.; Farrokhnia, R.A.; Matz, S.C.; Martens, D. Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records. Information 2021, 12, 518. https://doi.org/10.3390/info12120518

AMA Style

Ramon Y, Farrokhnia RA, Matz SC, Martens D. Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records. Information. 2021; 12(12):518. https://doi.org/10.3390/info12120518

Chicago/Turabian Style

Ramon, Yanou, R.A. Farrokhnia, Sandra C. Matz, and David Martens. 2021. "Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records" Information 12, no. 12: 518. https://doi.org/10.3390/info12120518

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