Summary of Towards Responsible Ai in Banking: Addressing Bias For Fair Decision-making, by Alessandro Castelnovo
Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making
by Alessandro Castelnovo
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG); Applications (stat.AP)
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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 This thesis tackles the pressing issue of bias and fairness in artificial intelligence (AI) decision-making processes, particularly in the banking sector where AI-driven decisions have significant societal implications. The seamless integration of fairness, explainability, and human oversight is crucial for establishing “Responsible AI.” The research emphasizes the importance of addressing biases within corporate culture that aligns with AI regulations and universal human rights standards, especially in automated decision-making systems. Embedding ethical principles into AI model development, training, and deployment is critical for compliance with forthcoming European regulations and promoting societal good. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make AI more fair and trustworthy. It focuses on understanding, mitigating, and accounting for bias in AI decisions. The research uses real-world scenarios to test its ideas and works with a bank to apply the results. This helps us understand fairness better and provides practical tools for making responsible AI decisions. The researchers share their code as open-source packages, which can help others make progress on AI fairness. |
Keywords
* Artificial intelligence * Embedding
