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technology15h ago
Overcoming the algorithmic gender bias in AI-driven personal finance
- Latest finding: AI-driven lending can widen gaps when sex-disaggregated data is not used to monitor outcomes in finance.
- Kenya example shows women often receive smaller loans than men despite better repayment performance.
- Sex-disaggregated data is essential to reveal who has access to finance and where outcomes diverge.
- Some Latin American and African regulators have tracked sex-disaggregated banking data for years.
- Mexico and Chile show how gender data can drive policy adjustments in lending risk and provisioning.
- EU policy discussions focus on regulating algorithmic finance to address gender fairness.
- The piece argues data is a practical tool for accountability in digital finance.
- Gender visibility in financial data is not symbolic but necessary for fair finance.
- High-risk AI governance in the EU remains a work in progress with gaps in practice.
- The author previews a policy paper on sex-disaggregated data in finance.
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