Can You Really Trust AI Explanations?

artificial intelligence, explainability is the holy grail. We want to understand why models make the decisions they do. But the trust we place in these explanations might be misplaced, especially when dealing with complex tabular data.

Questioning AI Explainability

A recent study puts the spotlight on the reliability of local explainability techniques like Local Interpretable Model-Agnostic Explanations (LIME), Kernel SHapley Additive exPlanations (SHAP), and Feature Ablation. Evaluated across 32 datasets, these methods were tested against various machine learning models. Yet, the results are far from comforting.

It turns out that the explanations these tools provide often aren't aligned with the model's true predictive powers. So, what are they reflecting instead? Dataset complexity and feature distributions appear to be the true puppeteers, manipulating the quality and reliability of the explanations.

Metrics That Matter

Three main properties were under scrutiny: faithfulness to the model's predictions, robustness to input data variations, and the complexity of the explanation itself. It's a classic case of the more you know, the less you understand. Faithfulness isn't a given just because an explanation looks plausible.

In fact, the study's benchmarking efforts have unveiled two groups: consensus-correct, samples all models predicted correctly, and consensus-wrong, samples all models got wrong. Seems like a paradox, right? Why do explanations not correlate cleanly with model performance?

The Real Issue: Dataset Complexity

Here lies the kicker: the complexity of the dataset and how features are distributed within it are the main factors derailing explanation quality. It's not about whether the AI's right or wrong. It's about the intricacies of the data it's tackling. If the AI can hold a wallet, who writes the risk model?

So, why should we care? Because blindly trusting AI explanations without understanding their true underpinnings could lead to real-world consequences, especially in high-stakes sectors like finance or healthcare. Slapping a model on a GPU rental isn't a convergence thesis. It's time we demand more than just plausible explanations. We need verifiable, reliable insights.