Sreekanth Menon, VP and Global Leader of AI/ML Services at Genpact, discusses responsible AI practices and data governance policies. He emphasizes the importance of reliability, security, autonomy, accountability, and traceability in AI models. The podcast explores the use of prompts and structured approaches in generating AI models, and highlights the significance of responsible AI in highly regulated industries like finance and healthcare.
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Quick takeaways
Responsible AI involves considering eight pillars: domain-infused business metrics evaluation, fairness and legal compliance, interpretability and explainability, data and model pattern change mitigation, reliability and safety, privacy and security, autonomy and accountability, and traceability.
Responsible AI frameworks and assets are necessary to address issues such as hallucinations, misinformation, data privacy, fairness, interpretability, and regulatory compliance in sectors like banking.
Deep dives
Responsible AI as a Practical Data Governance Policy
Sreekanth Manan, VP and Global Leader of AIML services at GenPACT, discusses the importance of responsible AI practices in transparency and accountability. He stresses that responsible AI is not a new concept but has become more complex with the democratization of AI. Sreekanth explains that responsible AI involves considering eight pillars: domain-infused business metrics evaluation, fairness and legal compliance, interpretability and explainability, data and model pattern change mitigation, reliability and safety, privacy and security, autonomy and accountability, and traceability. He emphasizes the need for implementation of responsible AI at every stage of the AI development process.
Challenges and Trends in Responsible AI
Sreekanth highlights the challenges and trends in responsible AI, focusing on the importance of responsible gen AI for enterprise leaders. He discusses the need for responsible AI to address issues such as hallucinations and misinformation. Sreekanth explains that responsible AI requires considering the data layer, foundation model layer, fine-tuning layer, and application layer. He emphasizes the need for responsible AI frameworks and assets to ensure regulatory alignment, data privacy, fairness, interpretability, and more. The banking industry is highlighted as a sector where responsible AI is crucial due to regulatory requirements.
Practical Applications of Responsible AI
Sreekanth provides practical examples of responsible AI applications, such as credit reviews in the banking industry. He explains how responsible generative AI frameworks help stakeholders in building monitoring tools, ensuring regulatory alignment, data extraction, and contextualized data analysis. Sreekanth emphasizes the importance of responsible AI in data privacy, bias detection, reliability, model ownership, and accountability in different industries. He mentions the need for responsible AI to address issues like privacy, hallucinations, and biases, with specific reference to sectors such as consumer healthcare and high-tech manufacturing.
Implementing Responsible AI in Enterprises
In implementing responsible AI, Sreekanth advises enterprise leaders to create a responsible AI vision and strategy, develop policies and frameworks, and establish a governance body. He emphasizes the need for constant education and empowerment to ensure responsible AI becomes an integral part of organizational culture. He suggests that responsible AI should align with the enterprise's value statements and be embedded in every stage of AI development. Sreekanth concludes by emphasizing the importance of responsible AI audits and collaboration among different teams in the enterprise to ensure the successful implementation of responsible AI.
Today’s guest is Sreekanth Menon, Vice President and Global Leader of AI/ML Services at Genpact. Sreekanth joins Emerj CEO and Head of Research Daniel Faggella on today’s show for a candid and productive conversation on what responsible AI practices in transparency and accountability truly mean in terms of practical data governance policies. With new generative AI applications and challenges surrounding hallucinations and misinformation, responsible AI disciplines and workflows are no longer the terrain of PR buzzwords. Sreekanth’s appearance on today’s show builds off of Genpact’s existing thought leadership in these areas, offering business leaders actionable insights in leveraging these new capabilities for procurement process improvement. Along the way, he explains in-depth what these changes will mean for banking and other industries as they become more widespread between enterprises. For more relevant insights on these topics and more, business leaders should also explore Genpact’s recent white paper titled ‘Creativity and Constraints: A Framework for Responsible Generative AI.’ This episode is sponsored by Genpact. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.
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