Published Sep 14, 2020

When AI goes wrong

Explore the crucial themes of AI reliability and preparedness as experts discuss the challenges of discrimination, regulation, and incident response in AI systems. Gain insights on improving model debugging and interpretability to navigate the evolving legal landscape effectively.

Episode Highlights

Topics covered

Episode Highlights

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Dexa/Practical AI

When AI goes wrong

Topics covered

Popular Clips

Incident Response Checklist

The Future of AI Regulation

Addressing AI Discrimination

AI Incident Response

AI Law Insights

Importance of AI Preparedness

Practical AI Debugging

AI Law Firm

Interpretability and Decision-Making

Preparing for AI Incidents

Bridging the Gap

Anticipating AI Failures

AI Incident Response

Episode Highlights

AI Risks and Liabilities

Model Debugging Practices

AI Incident Response

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Eliminate AI failures

Success (and failure) in prompting

AI's impact on developers

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When data leakage turns into a flood of trouble

AI adoption in the enterprise

AI trailblazers putting people first

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When AI goes wrong

Topics covered

Popular Clips

Episode Highlights

AI Risks and LiabilitiesAI systems face significant challenges in addressing discrimination and navigating regulatory frameworks. Andrew and Patrick discuss the gaps in traditional incident response plans and the evolving legal landscape impacting AI.

AI Risks and Liabilities

Model Debugging PracticesThe discussion shifts to essential debugging techniques and the importance of interpretability in AI models. Patrick and Andrew share insights on improving AI reliability and understanding its decisions.

Model Debugging Practices

AI Incident Response

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