Summary of Best Practices For Responsible Machine Learning in Credit Scoring, by Giovani Valdrighi et al.
Best Practices for Responsible Machine Learning in Credit Scoring
by Giovani Valdrighi, Athyrson M. Ribeiro, Jansen S. B. Pereira, Vitoria Guardieiro, Arthur Hendricks, Décio Miranda Filho, Juan David Nieto Garcia, Felipe F. Bocca, Thalita B. Veronese, Lucas Wanner, Marcos Medeiros Raimundo
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a non-systematic literature review on developing responsible machine learning models in credit scoring, focusing on fairness, reject inference, and explainability. The authors discuss definitions, metrics, and techniques for mitigating biases and ensuring equitable outcomes across different groups. They also address the issue of limited data representativeness by exploring reject inference methods that incorporate information from rejected loan applications. Finally, they emphasize the importance of transparency and explainability in credit models, discussing techniques that provide insights into the decision-making process and enable individuals to understand and potentially improve their creditworthiness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to use machine learning to help people get loans without being unfair or biased. The authors looked at lots of other research papers to figure out what works best for making sure loan decisions are fair and transparent. They found some ways to make sure the models don’t treat different groups unfairly, like by looking at rejected loan applications too. They also think it’s important for people to understand how their credit scores work and why they get certain loans or not. |
Keywords
» Artificial intelligence » Inference » Machine learning
