#15: Digging into explainable AI

Explainability

The importance of explainability in AI is underscored by its role in building trust and reliability. Angelo Dalli highlights how neurosymbolic AI can anticipate outcomes, a key feature of intelligence, by predicting the impact of actions 1. This capability is crucial for applications like robotics, where understanding and predicting partner movements is essential. He explains that the technical challenges of creating such AI have been significant, requiring new mathematical frameworks and theoretical models 1.

People have been trying to solve this for over 30 years... it involves quite a kind of a lot of rethink on how things work.

--- Angelo Dalli

Additionally, the speed and efficiency of AI models are improved, allowing them to run on smaller, less powerful devices without compromising performance 2.

   

Practical Applications

Explainability in AI is not just theoretical but has practical applications in various industries. Angelo Dalli describes how AI can provide reasoning for its decisions, enhancing trust by explaining why it gives certain answers 3. This human-like understanding is crucial for AI to be seen as a reliable partner in decision-making processes. He points out that symbolic AI can address current limitations by creating rules that reflect real-world knowledge, such as knowing a person has five fingers 3.

It will actually give you the reasoning. If you just want a quick answer, you don't need the explanation. But if you're interested in the explanation, it can actually explain to you why.

--- Angelo Dalli

This capability is particularly valuable in regulated industries like fintech, where explainable AI can significantly reduce errors and save costs 4.