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Gabriela Bodea,, Clash-ul crizelor sau viclenia lumii asimetrice (Ediția a doua), Presa Universitară Clujeană, 2023
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Moldovan, D. (2023) IEEE Access [Info Economics, Q2]

Autor: Ovidiu Ioan Moisescu

Publicat: 12 Iulie 2023


Moldovan, D. (2023) Algorithmic Decision Making Methods for Fair Credit Scoring. IEEE Access, 11, 59729-59743.

DOI: https://doi.org/10.1109/ACCESS.2023.3286018

✓ Publisher: IEEE
✓ Categories: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications
✓ Article Influence Score (AIS): 0.685 (2022) / Q2 in all categories

Abstract: The effectiveness of machine learning in evaluating the creditworthiness of loan applicants has been demonstrated for a long time. However, there is concern that the use of automated decision-making processes may result in unequal treatment of groups or individuals, potentially leading to discriminatory outcomes. This paper seeks to address this issue by evaluating the effectiveness of 12 leading bias mitigation methods across 5 different fairness metrics, as well as assessing their accuracy and potential profitability for financial institutions. Through our analysis, we have identified the challenges associated with achieving fairness while maintaining accuracy and profitabiliy, and have highlighted both the most successful and least successful mitigation methods. Ultimately, our research serves to bridge the gap between experimental machine learning and its practical applications in the finance industry.



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