⚖️Responsible AI Practices

The podcast is an interview with a candidate discussing their expertise in Responsible AI practices.

candidate outlines a comprehensive approach that integrates fairness, interpretability, and robustness throughout the machine learning lifecycle, referencing legal and industry frameworks such as GDPR and Google's PAIR.

They detail specific techniques and metrics used for bias detection and mitigation, explain the trade-offs between different fairness definitions, and describe methods for handling black-box models through interpretability tools like LIME and SHAP.

The candidate also elaborates on their strategies for ensuring robustness against data shifts and adversarial attacks, emphasizing continuous monitoring, incident response protocols, and the importance of governance structures and cross-functional collaboration to maintain ethical AI in production.

Finally, they articulate how to communicate the value and ROI of Responsible AI to non-technical stakeholders by linking it to tangible business outcomes like cost reduction and risk mitigation.