Navigating Responsible AI: Bridging Theory and Practice
In my recent talk at the NASSCOM CXO Breakfast in Chandigarh, I explored the challenging intersection of ideals and reality in Responsible AI. While terms like fairness and transparency dominate the discourse, actual implementation encounters significant hurdles, such as business pressures and data limitations.
Key Insights:
- Responsible AI principles can often clash with enterprise needs.
- Two Types of AI in Business:
- Internal-facing: Employee productivity, SDLC tools.
- External-facing: Chatbots, customer service applications.
Utilizing the NIST AI Risk Management Framework (RMF) helps in:
- Govern: Establish ethical policies and accountability.
- Map: Identify risks linked to AI use cases.
- Measure: Evaluate performance and trustworthiness.
- Manage: Develop strategies to mitigate risks effectively.
In this rapidly evolving landscape, fostering a culture of Responsible AI is crucial. Implement policies, train teams, and ensure accountability.
Let’s drive the conversation on Responsible AI! Share your insights below and continue the dialogue!
