Bias and Governance
Reducing bias in AI requires a strategic focus on areas that can cause the most harm, while fostering a culture that embraces the identification and resolution of issues. Continuous monitoring at runtime is essential, as is a holistic view of systems beyond just models. Engaging with real-world applications can uncover deeper ethical challenges and present intriguing data science puzzles to solve.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
The EU AI Act and Mitigating Bias in Automated Decisioning with Peter van der Putten - 699
Related Questions
Can you explain more strategies to manage AI bias as discussed in the episode "The EU AI Act and Mitigating Bias in Automated Decisioning with Peter van der Putten - 699" and the clip "Fairness in AI"?
Can you explain more strategies to manage AI bias as discussed in the episode The EU AI Act and Mitigating Bias in Automated Decisioning with Peter van der Putten - 699 and the clip Fairness in AI?
Can you explain more strategies to manage AI bias?