The aim of this paper is to propose a structural model that can explain the reasoning underlying automated decisions and, in particular, their unfairness. Moving beyond black-box approaches, our model provides transparency and interpretability, enabling a deep understanding of decision-making processes. Specifically, we build a diffusion process to explain the inequality and unfairness in credit lending. We then compare the Gini index before and after the application of the model. A substantial reduction in the Gini index indicates that the diffusion process can explain unfairness.

A structural model to explain unfairness

Giudici, Paolo
;
Pavarana, Simone;
2025-01-01

Abstract

The aim of this paper is to propose a structural model that can explain the reasoning underlying automated decisions and, in particular, their unfairness. Moving beyond black-box approaches, our model provides transparency and interpretability, enabling a deep understanding of decision-making processes. Specifically, we build a diffusion process to explain the inequality and unfairness in credit lending. We then compare the Gini index before and after the application of the model. A substantial reduction in the Gini index indicates that the diffusion process can explain unfairness.
2025
Proceedings of the International Joint Conference on Neural Networks
Inglese
1
9
9
Institute of Electrical and Electronics Engineers Inc.
345 E 47TH ST, NEW YORK, NY 10017 USA
2 Contributo in Volume::2.1 Contributo in volume (Capitolo o Saggio)
4
268
none
Giudici, Paolo; Neelakantan, Parvati; Pavarana, Simone; Schmidt, Thorsten
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1547360
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