From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing

Face recognition systems can now explain their decisions in plain language: The framework converts technical AI outputs into natural language explanations and visual highlights, allowing users to understand which facial features (like eyes, nose, or cheekbones) the system relied on when comparing two faces—making "black box" decisions transparent.

Interactive explanations adapt to what users want to know: Rather than providing generic explanations, the system functions as a chatbot that answers specific user questions about why a face was recognized or rejected, allowing people with different backgrounds to get the level of detail they need.

Transparency doesn't require sacrificing accuracy: Unlike some explainability approaches that reduce system performance, this method maintains the original face recognition accuracy while adding interpretability—addressing a key tension in responsible AI between performance and accountability.

Visual and textual explanations work together: The system combines saliency heatmaps (visual highlighting of important facial regions) with natural language descriptions, providing multiple ways for users to understand decisions and potentially catch unfair or biased outcomes.

This approach supports accountability and fairness auditing: By making face recognition decisions interpretable and interactive, the framework enables stakeholders—including regulators, auditors, and affected individuals—to better assess whether the system is making fair decisions and to identify potential sources of bias.