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NIST has presented AI standards for a risk management framework (AI RMF) that can be customized by organizations. AI risks emerge from the interplay of technical aspects combined with societal factors related to how an AI-driven system is used, its interactions with other AI systems, who operates it, and the social context in which it is deployed. Most data science teams are familiar with the regulations around traditional Model Risk Management and other forms of risk management already in place. The reality of AI, as per NIST report, is that without proper controls, the deployment can amplify and perpetuate inequitable and undesirable outcomes for individuals and communities. NIST recommends the need for social and professional responsibilities as a recommendation of their AI RMF framework. How should the risk governance program safeguard strategic investments in AI-driven automation and self service application adoption? It matters what systems you design, how you deploy and how the outcomes are used. The session reviews some key aspects of the AI risk management playbook using common Financial use cases to review how professional responsibilities will change with significant addition of AI solutions. What is non-negotiable as a part of any AI-data strategy and how the work has a greater impact on social outcomes.
Key Takeaways
1. Understanding the pillars of the AI Risk Management Framework
2. Framing the risks associated with AI
3. Social responsibility, professional responsibility
4. Map, measure, manage, govern process for AI RMF
5. Use cases to understand it all
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Priya Sarathy has 20+ years of experience as a thought leader and has successfully delivered innovative solutions across Fortune 500 companies in telecom, technology, and financial services. She has built highly productive teams to deliver cutting edge customer and business-centric solutions to drive new revenue opportunities. She is an evangelist for designing data strategies in collaboration with technology and business partners to ensure appropriate and relevant environment for big data use and AI/ML applications is built. As VP for data strategy in Identity and Fraud solutions at Equifax, she designed the framework for a real time data ecosystem to support responsible and accountable use of AI/ML-driven decisioning. Her work is captured in the patent application for a system for risk profiling. She advises non-profits around data management and AI/ML best practices to help them establish solid data foundation to grow fast.
Priya advocates the need for data and AI/ML literacy and awareness around cultural shifts needed within an organization for successful AI/ML strategy adoption. She mentors students and early career analytic professionals, volunteers with Women in Identity (WID) and local non-profit organizations. She is a board member for INFORMS Georgia and the National INFORMs Analytics society. She started Wheel Data Strategies to guide organizations to be more effective in harnessing their data and analytics resources to meet business expectations.
