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A 2020 Brookings policy report relevant to AI fairness and governance in high-stakes domains; useful for understanding algorithmic bias risks in financial services and the regulatory frameworks needed to address them.
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Summary
This Brookings Institution report by Aaron Klein proposes a framework to evaluate the impact of AI in consumer lending, examining whether AI will reduce or exacerbate existing biases in credit allocation. It argues that AI presents a rare opportunity to build fairer, more inclusive financial systems, but also risks reinforcing discriminatory patterns while making bias harder to detect.
Key Points
- •AI in consumer lending can either reduce historical credit bias or entrench it further through opaque algorithmic decision-making.
- •Traditional credit reporting systems perpetuate existing racial and socioeconomic bias; AI could bypass these but may replicate them via proxies.
- •The paper proposes a governance framework for evaluating AI fairness in consumer lending contexts.
- •Discriminatory outcomes in AI-based lending may be harder to detect than in traditional systems, raising accountability concerns.
- •Policy interventions are needed to ensure AI-driven credit systems expand access rather than reinforce exclusion.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Driven Institutional Decision Capture | Risk | 73.0 |
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Reducing bias in AI-based financial services | Brookings
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Research
Reducing bias in AI-based financial services
Aaron Klein
Aaron Klein
Miriam K. Carliner Chair
- Economic Studies ,
Senior Fellow
- Center on Regulation and Markets
July 10, 2020
19 min read
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Editor's note:
This report from The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative is part of “ AI Governance ,” a series that identifies key governance and norm issues related to AI and proposes policy remedies to address
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