The article concludes by emphasizing the importance of AI in the lending industry, as it enables more people to access credit fairly and accurately. It recommends diligent testing, monitoring, and human review in the use of AI to unlock new growth opportunities for companies and produce better outcomes for customers. It also stresses that AI is not a "black box" and companies can manage all factors, including how it scores and optimizes and the frequency of model updates.
Key takeaways:
- Bias in AI, particularly in financial services, is a major concern as algorithms can potentially embed discriminatory practices into automated credit decisions.
- Companies can control bias by building technical and operational controls into their AI approach, ensuring that data like race, gender or other characteristics aren’t considered in the algorithm.
- Regular review, testing, and monitoring of AI models are critical in de-biasing AI. Human decision-makers play a crucial role in reviewing the final decision to ensure fairness and equity.
- AI is not a “black box” and companies can manage all factors, including how it scores and optimizes and the frequency of model updates. The frequency of model updates allows companies to intervene in any issues before deploying changes.