Understanding surrogate explanations: the interplay between complexity, fidelity and coverage
This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings. We start our exposition by considering global surrogates, describing the trade-off between complexity of the surrogate and fidelity to the black-box being modelled. We show that transitioning from global to local - reducing coverage - allows for more favourable conditions on the Pareto frontier of fidelity-complexity of a surrogate. We discuss the interplay between complexity, fidelity and coverage, and consider how different user needs can lead to problem formulations where these are either constraints or penalties. We also present experiments that demonstrate how the local surrogate interpretability procedure can be made interactive and lead to better explanations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Private Graph Extraction via Feature Explanations
Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal …
BIG-bench Machine LearningGraph ReconstructionMusicLIME: Explainable Multimodal Music Understanding
Multimodal models are critical for music understanding tasks, as they capture the complex interplay between audio and lyrics. However, as these models become more prevalent, the need for explainability grows-understandin…
Decision MakingFairnessFeature ImportanceMachine Explanations and Human Understanding
Explanations are hypothesized to improve human understanding of machine learning models and achieve a variety of desirable outcomes, ranging from model debugging to enhancing human decision making. However, empirical stu…
Decision MakingOpen-Ended Question AnsweringInterpreting Black-box Machine Learning Models for High Dimensional Datasets
Deep neural networks (DNNs) have been shown to outperform traditional machine learning algorithms in a broad variety of application domains due to their effectiveness in modeling complex problems and handling high-dimens…
Vocal Bursts Intensity PredictionOn the Complexity-Faithfulness Trade-off of Gradient-Based Explanations
ReLU networks, while prevalent for visual data, have sharp transitions, sometimes relying on individual pixels for predictions, making vanilla gradient-based explanations noisy and difficult to interpret. Existing method…