What is explainable AI used for?#
By making an AI's decision-making process transparent, XAI builds trust with users, executives, and regulators. This allows businesses to confidently deploy AI for critical functions, ensure fairness, debug models more effectively, and meet legal requirements for accountability.
How does explainable AI work?#
Explainable AI works in one of two main ways. The first is by using AI models that are inherently simpler and more transparent, where the logic is straightforward enough for a person to follow. While less complex, these models are well-suited for situations where justification is paramount, such as a loan application decision.
The second approach involves applying special techniques to more complex, "black box" models. After the AI makes a decision, these techniques analyse the process to highlight the most influential factors. For example, in an AI that predicts customer churn, an XAI tool could show that "number of support tickets logged" and "recent price increase" were the top two reasons a specific customer was flagged as likely to leave. This doesn't reveal the entire complex calculation, but it provides a practical, human-understandable rationale.
How is explainable AI different from black-box AI?#
Explainable AI focuses on transparency and interpretability in a model's decision-making process. This stands in contrast to "black box" AI models, such as many deep learning systems, where the primary focus is on achieving the highest possible accuracy, even if the internal logic is too complex for humans to comprehend. While a black box model might provide a highly accurate prediction, an explainable model provides a slightly less accurate prediction but also tells you why it made that prediction, which is often more valuable for critical business decisions.
Does an explanation make a decision better?#
Not on its own. Bansal and colleagues found that explanations raised people's acceptance of AI advice whether that advice was right or wrong — the explanation increased confidence without improving discrimination. An interface that explains well and is trusted uncritically has moved the risk rather than reduced it.
That is the gap between explanation and human oversight, which is a state in which a person understands enough to judge what the system is doing, not a box they ticked. Closing it is the work of comprehension engineering: designing what reaches the person so that the judgment they have to make is one they are actually equipped to make.