Artificial Intelligence Risk & Governance
In affiliation with the Professor Kartik Hosanagar, Artificial Intelligence/Machine Learning Risk and Security (AIRS) believes there are significant potential benefits of AI and that its adoption within financial services presents opportunities to improve both business and societal outcomes when risks are managed responsibly. This white paper provides AIRS views on potential approaches to AI governance for financial services including potential risks, risk categorization, interpretability, discrimination, and risk mitigation.
Key Takeaways
- AIRS believes there are significant potential benefits of AI and that its adoption within financial services presents opportunities to improve both business and societal outcomes when risks are managed responsibly.
- This paper explores the potential risks of AI and provides a standardized practical categorization of these risks: Data Related Risks, AI/ML Attacks, Testing and Trust, and Compliance
- AI governance frameworks could help organizations learn, govern, monitor, and mature AI adoption. Four core components of AI governance are: definitions, inventory, policy/standards, and a governance framework, including controls.
- AI, in certain use cases, could lead to privacy issues, and/or potentially discriminatory or unfair outcomes, if not implemented with appropriate care. We explore, in detail, the subject of interpretability and discrimination in using AI for certain use cases.
- While there is no one-size-fits-all approach, practices institutions might consider adopting to mitigate AI risk include oversight and monitoring, enhancing explainability and interpretability, as well as exploring the use of evolving risk-mitigating techniques like differential privacy, and watermarking, among others.