Aligned explanations in neural networks

Explanations should construct predictions, not justify them: Most AI explanation methods today work backwards—they rationalize why a model made a decision after the fact. This paper argues for "explanatory alignment," where explanations are the actual mechanism driving the prediction, fundamentally changing how we can trust AI systems' reasoning.

PiNets bridge deep learning power with interpretability: The proposed Pointwise-interpretable Networks architecture combines the predictive accuracy of complex neural networks with the transparency of simple linear models by creating customized linear models for each individual prediction, making it possible to see exactly what features matter for each decision.

Four measurable standards for trustworthy explanations: The paper establishes MARS criteria—meaningfulness, alignment, robustness, and sufficiency—to evaluate whether explanations are genuinely faithful to how a model works. This provides concrete benchmarks for assessing whether an AI system's explanations can actually be trusted.

Tested on real-world visual tasks: The approach was validated on image classification and segmentation problems, demonstrating that faithful explanations are achievable in complex domains, not just simple datasets, which is crucial for real-world applications like medical imaging or autonomous systems.

Foundation for scientific and critical applications: By reconciling accuracy with interpretability, this work enables AI to be used more responsibly in high-stakes domains where understanding why a decision was made is as important as the decision itself.