Non-Uniform Interpolation in Integrated Gradients for Low-Latency Explainable-AI

Explainability doesn't have to be slow: Integrated Gradients, a popular method for explaining AI decisions, typically requires many computational passes through a model. This paper demonstrates a 2.6-3.6× speedup on GPU systems, making real-time explanations more feasible for practical applications where users need immediate feedback about why an AI made a decision.

Smart shortcuts maintain accuracy: The researchers achieved faster explanations by using non-uniform interpolation instead of uniform steps—essentially taking fewer but strategically-placed computational samples. Importantly, this optimization maintained convergence quality with only 0.2-3.2% additional overhead, suggesting that explanations remain reliable even when accelerated.

Hardware matters for responsible AI: The optimization was specifically designed with GPU hardware capabilities in mind, highlighting that responsible AI isn't just about algorithms—it's also about practical implementation. This approach could help organizations deploy explainable AI systems in real-world settings where computational resources and response time are constraints.

Explainability at scale becomes more achievable: By reducing the computational burden of generating explanations, this work addresses a key barrier to widespread adoption of XAI methods. Organizations can now potentially explain more model decisions without proportionally increasing computational costs, supporting more transparent AI systems.