AI Explainability and the Need for Justification

AI is increasingly central to effective market surveillance, but as models grow more sophisticated, the need for transparency becomes critical. Episode 6 examines how explainability helps bridge the gap between complex algorithms and human understanding. By providing insights into the factors and data points influencing each alert, explainable AI enables surveillance teams to validate decisions, challenge assumptions, and ensure outcomes are fair and unbiased.

The episode also explores the operational impact of justification. When alerts come with clear reasoning, investigations become faster and more efficient, enabling teams to prioritise genuinely suspicious activity. This reduces noise, improves accuracy, and ultimately leads to more reliable detection of potential manipulation.

Explainability is not just a regulatory expectation, it is essential for maintaining trust in AI-powered systems. As Aquis continues to innovate in market surveillance, ensuring our models are interpretable, auditable, and accountable remains a core priority.