There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term. We revisit faithfulness in the context of continuous data and propose two formal definitions of faithfulness for feature attribution methods. Qualitative faithfulness demands that scores reflect the true qualitative effect (positive vs. negative) of the feature on the model and quanitative faithfulness that the magnitude of scores reflect the true quantitative effect. We discuss under which conditions these requirements can be satisfied to which extent (local vs global). As an application of the conceptual idea, we look at differentiable classifiers over continuous data and characterize Gradient-scores as follows: every qualitatively faithful feature attribution method is qualitatively equivalent to Gradient-scores. Furthermore, if an attribution method is quantitatively faithful in the sense that changes of the output of the classifier are proportional to the scores of features, then it is either equivalent to gradient-scoring or it is based on an inferior approximation of the classifier. To illustrate the practical relevance of the theory, we experimentally demonstrate that popular attribution methods can fail to give faithful explanations in the setting where the data is continuous and the classifier differentiable.
Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance
TU Delft（代尔夫特理工大学）；XITASO GmbH（XITASO有限公司）；University College Cork（科克大学学院）；RPTU University Kaiserslautern-Landau（凯撒斯劳滕-兰道工业大学）；DFKI（德国人工智能研究中心）；TU Dresden（德累斯顿理工大学）
Faithful or Just Plausible? Evaluating the Faithfulness of Closed-Source LLMs in Medical Reasoning
Stratified Precision；Harvard Medical School（哈佛医学院）；Imperial College London（帝国理工学院伦敦分校）；National Health Service（国家健康服务系统）；Ipsen France（Ipsen法国）；University College London（伦敦大学学院）
Measuring Faithfulness Depends on How You Measure: Classifier Sensitivity in LLM Chain-of-Thought Evaluation
University of Nevada, Las Vegas, Department of Management, Entrepreneurship and Technology, Lee Business School（内华达大学拉斯维加斯分校管理、创业与技术系，Lee商学院）；DeepNeuro AI
FaithLM: Towards Faithful Explanations for Large Language Models
Rice University（里士大学）；Texas A&M University（德克萨斯农工大学）；Wake Forest University（沃杰森林大学）；New Jersey Institute of Technology（新泽西理工学院）；John Hopkins University（约翰霍普金斯大学）；Google Research（谷歌研究）；Meta Platforms, Inc.（元宇宙平台公司）
