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The AI in Business Podcast

Creativity and Constraints: A Framework for Responsible Generative AI - with Sreekanth Menon of Genpact

Feb 5, 2024
38:29
Snipd AI
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.

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