Algorithmic Explainability Issues
The discussion highlights the misconception that algorithmic explainability alone can address broader ethical concerns in AI, such as bias and fairness. While many seek technological solutions to complex problems, true success lies in a comprehensive approach that adheres to solid methodologies. Understanding past AI failures is crucial for avoiding similar pitfalls in future projects.In this clip
From this podcast
AI Today Podcast
AI Today Podcast: Ethical & Responsible AI Series: Explainable and Interpretable AI
Related Questions
What are the key topics in AI interpretability as discussed in the episode AI Today Podcast: Ethical & Responsible AI Series: Explainable and Interpretable AI and the clip AI Explainability Framework?
What is the challenge around explainability in AI as discussed in the episode AI Today Podcast #015: AI Explainability & More – Interview with Mark van Rijmenam and the clip Building Responsible AI?
What is the challenge around explainability in AI as discussed in the episode AI Today Podcast: How Data Privacy is Impacting AI – Interview with Rajeev Sambyal, BNY Mellon and the clip Trust in AI?