As AI systems have become more complex, particularly deep neural networks with millions or billions of parameters, it has become harder to understand exactly why a system made a specific decision. Explainable AI is the field focused on solving this problem.
Why Explainability Matters
If a loan application is denied by an AI system, both the applicant and the bank need to understand why, not just to satisfy curiosity, but because regulations in many regions require a clear justification for such decisions, and because unexplainable errors are far harder to identify and correct.
The Black Box Problem
Many of the most powerful AI models, particularly large neural networks, are often described as 'black boxes' because their internal decision-making process, spread across millions of weighted connections, is not easily interpretable even by the engineers who built them.
Approaches to Explainability
- Using inherently interpretable models, like decision trees, in situations where transparency matters more than raw accuracy.
- Building separate explanation tools that analyse a complex model's behaviour after the fact to approximate why it made a particular decision.
- Highlighting which parts of an input, such as specific words in a document or regions of an image, most influenced a given prediction.
Explainable AI remains an active area of research, and finding the right balance between a model's raw predictive accuracy and how easily its decisions can be explained is one of the ongoing tensions in deploying AI responsibly.