Episode 31. Explainable AI: Opening the black box of deep learning
In this episode, Jamie and Rachel dive into the complexities of explainable AI, exploring the challenges of understanding how deep learning models make decisions. They discuss the ethical and legal implications, the trade-off between transparency and performance, and the emerging solutions in hybrid models. Tune in for a thought-provoking conversation on why explainability is crucial for the future of AI. Donā€™t forget to subscribe, share, and explore additional AI resources at bit.ly/turingtestpodcast.
