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The AI in Business Podcast
Fostering Responsible AI Outcomes through Operations and Developer Workflows - with Ranjan Sinha of IBM
May 25, 2024
18:17
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IBM Fellow, Ranjan Sinha discusses driving responsible AI through operations, emphasizing end-to-end lifecycle tracking, automated processes, and scalability. Topics include managing biases, data quality challenges, empowering employees for equitable AI outcomes, developers using AI tools for productivity, and evolution of AI in enterprises.
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Quick takeaways
- Operationalizing AI with trust requires end-to-end lifecycle tracking and scalable automated processes.
- Training AI models on diverse data and setting constraints prevent hallucinations, ensuring accurate outputs.
Deep dives
Challenges of Operationalizing AI with Confidence and Trust
Operationalizing AI with confidence and trust poses challenges including tracking the end-to-end AI lifecycle with transparent processes and ensuring scalability. Constructing models without clarity, monitoring, or transparency leads to biases, posing concerns for AI stakeholders. To address these issues, developing robust governance involving people, processes, technology, and high-quality training data is crucial.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode