What are the main causes of discrimination in lending and credit scoring as discussed in the chapter?
Discrimination in lending and credit scoring is mainly caused by biased historical data, flawed algorithms, and structural inequities.
The main causes of discrimination in lending and credit scoring include the use of historical data that contain structural and racial biases, which algorithms then learn from and perpetuate. Additionally, flawed algorithms may associate certain demographics with higher risk, leading to discriminatory outcomes. Structural inequities in the financial system also contribute to these issues. Addressing discrimination requires transparent AI models, regulatory frameworks for fairness, and inclusive data practices to mitigate historical biases.
Key points
- Biased historical data perpetuate structural and racial biases.
- Flawed algorithms associate certain demographics with higher risk.
- Structural inequities in the financial system contribute to discrimination.
- Transparent AI models are needed for auditing and fairness.
- Regulatory frameworks should enforce fairness in algorithms.
- Inclusive data practices can help reduce historical bias.
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AI and ML Governance in Financial Services
Richard Gwashy Young
Routledge, Taylor & Francis Group