Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
Machine learning methods have significantly improved in their predictive capabilities, but at the same time they are becoming more complex and less transparent. As a result, explainers are often relied on to provide interpretability to these black-box prediction models. As crucial diagnostics tools, it is important that these explainers themselves are robust. In this paper we focus on one particular aspect of robustness, namely that an explainer should give similar explanations for similar data inputs. We formalize this notion by introducing and defining explainer astuteness, analogous to astuteness of prediction functions. Our formalism allows us to connect explainer robustness to the predictor's probabilistic Lipschitzness, which captures the probability of local smoothness of a function. We provide lower bound guarantees on the astuteness of a variety of explainers (e.g., SHAP, RISE, CXPlain) given the Lipschitzness of the prediction function. These theoretical results imply that locally smooth prediction functions lend themselves to locally robust explanations. We evaluate these results empirically on simulated as well as real datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Probabilistic Lipschitzness and the Stable Rank for Comparing Explanation Models
Explainability models are now prevalent within machine learning to address the black-box nature of neural networks. The question now is which explainability model is most effective. Probabilistic Lipschitzness has demons…
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
In Graph Neural Networks (GNNs), the graph structure is incorporated into the learning of node representations. This complex structure makes explaining GNNs' predictions become much more challenging. In this paper, we pr…
PredictionGraph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, ow…
counterfactualCounterfactual ExplanationCounterfactual ReasoningVulnerability DetectionLaPLACE: Probabilistic Local Model-Agnostic Causal Explanations
Machine learning models have undeniably achieved impressive performance across a range of applications. However, their often perceived black-box nature, and lack of transparency in decision-making, have raised concerns a…
Decision MakingFairnessmodelModel SelectionVerifying Relational Explanations: A Probabilistic Approach
Explanations on relational data are hard to verify since the explanation structures are more complex (e.g. graphs). To verify interpretable explanations (e.g. explanations of predictions made in images, text, etc.), typi…
counterfactual