What are your thoughts on MIT Researchers’ developed new method that uses Artificial Intelligence to Automate the Explanation of Complex Neural Networks? Will this bridge the gap of the transparency, explainability and contestability (TEC) in GenAI, thus improving adoption?

What's the level of basic knowledge required to understand the explanation. Weights and other results of a learning process can be graphically examined but it still needs some know-how on the ML model approach.

The focus of this work is more on the interpretability. It will help some certain audience and not necessarily useful for any users of technologies based on neural networks. It is also important to notice that although transparency, explainability and contestability are closely related but they are different concepts and advancements in one doesn't necessarily address the challenges in another. 

This method involves "automated interpretability agents" (AIAs) which, like scientists, hypothesize, test, and learn iteratively. While this method marks significant progress in AI's self-explanation, it's not fully there yet. AIAs can explain many, but not all, network functions accurately, especially in more complex or noisy areas. This step towards demystifying AI workings could greatly boost trust and adoption in generative AI, showing the potential for more intuitive and transparent AI systems.

May please share the Paper are you referring to?

Thanks.