Accuracy Isn’t Enough
Ethical Machines
00:00
Understanding AI Explainability
This chapter examines the critical concept of explainability in algorithms, especially in large language models. It draws parallels between human brain functioning and neural networks, emphasizing the need for transparency in AI decision-making processes. The discussion highlights the complexities and potential pitfalls of relying on flawed reasoning in AI systems, advocating for deeper investment in understanding model structure for improved reliability.
Transcript
Play full episode
