Robustness and Usefulness in AI Explanation Methods

• URL: http://arxiv.org/abs/2203.03729v1
• Date: Mon, 7 Mar 2022 21:30:48 GMT
• Title: Robustness and Usefulness in AI Explanation Methods
• Authors: Erick Galinkin
• Abstract summary: This work summarizes, compares, and contrasts three popular explanation methods: LIME, SmoothGrad, and SHAP. We evaluate these methods with respect to: robustness, in the sense of sample complexity and stability; understandability, in the sense that provided explanations are consistent with user expectations. This work concludes that current explanation methods are insufficient; that putting faith in and adopting these methods may actually be worse than simply not using them.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainability in machine learning has become incredibly important as machine learning-powered systems become ubiquitous and both regulation and public sentiment begin to demand an understanding of how these systems make decisions. As a result, a number of explanation methods have begun to receive widespread adoption. This work summarizes, compares, and contrasts three popular explanation methods: LIME, SmoothGrad, and SHAP. We evaluate these methods with respect to: robustness, in the sense of sample complexity and stability; understandability, in the sense that provided explanations are consistent with user expectations; and usability, in the sense that the explanations allow for the model to be modified based on the output. This work concludes that current explanation methods are insufficient; that putting faith in and adopting these methods may actually be worse than simply not using them.

Related papers

• An AI Architecture with the Capability to Explain Recognition Results [0.0]
  This research focuses on the importance of metrics to explainability and contributes two methods yielding performance gains. The first method introduces a combination of explainable and unexplainable flows, proposing a metric to characterize explainability of a decision. The second method compares classic metrics for estimating the effectiveness of neural networks in the system, posing a new metric as the leading performer.
  arXiv  Detail & Related papers  (2024-06-13T02:00:13Z)
• Introducing User Feedback-based Counterfactual Explanations (UFCE) [49.1574468325115]
  Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in XAI. UFCE allows for the inclusion of user constraints to determine the smallest modifications in the subset of actionable features. UFCE outperforms two well-known CE methods in terms of textitproximity, textitsparsity, and textitfeasibility.
  arXiv  Detail & Related papers  (2024-02-26T20:09:44Z)
• Explainability for Machine Learning Models: From Data Adaptability to User Perception [0.8702432681310401]
  This thesis explores the generation of local explanations for already deployed machine learning models. It aims to identify optimal conditions for producing meaningful explanations considering both data and user requirements.
  arXiv  Detail & Related papers  (2024-02-16T18:44:37Z)
• Evaluating the Utility of Model Explanations for Model Development [54.23538543168767]
  We evaluate whether explanations can improve human decision-making in practical scenarios of machine learning model development. To our surprise, we did not find evidence of significant improvement on tasks when users were provided with any of the saliency maps. These findings suggest caution regarding the usefulness and potential for misunderstanding in saliency-based explanations.
  arXiv  Detail & Related papers  (2023-12-10T23:13:23Z)
• Learning Physical Concepts in Cyber-Physical Systems: A Case Study [72.74318982275052]
  We provide an overview of the current state of research regarding methods for learning physical concepts in time series data. We also analyze the most important methods from the current state of the art using the example of a three-tank system.
  arXiv  Detail & Related papers  (2021-11-28T14:24:52Z)
• This is not the Texture you are looking for! Introducing Novel Counterfactual Explanations for Non-Experts using Generative Adversarial Learning [59.17685450892182]
  counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image. We present a novel approach to generate such counterfactual image explanations based on adversarial image-to-image translation techniques. Our results show that our approach leads to significantly better results regarding mental models, explanation satisfaction, trust, emotions, and self-efficacy than two state-of-the art systems.
  arXiv  Detail & Related papers  (2020-12-22T10:08:05Z)
• Evaluating Explanations: How much do explanations from the teacher aid students? [103.05037537415811]
  We formalize the value of explanations using a student-teacher paradigm that measures the extent to which explanations improve student models in learning. Unlike many prior proposals to evaluate explanations, our approach cannot be easily gamed, enabling principled, scalable, and automatic evaluation of attributions.
  arXiv  Detail & Related papers  (2020-12-01T23:40:21Z)
• Reflective-Net: Learning from Explanations [3.6245632117657816]
  This work provides the first steps toward mimicking this process by capitalizing on the explanations generated based on existing explanation methods, i.e. Grad-CAM. Learning from explanations combined with conventional labeled data yields significant improvements for classification in terms of accuracy and training time.
  arXiv  Detail & Related papers  (2020-11-27T20:40:45Z)
• Efficient computation of contrastive explanations [8.132423340684568]
  We study the relation of contrastive and counterfactual explanations. We propose a 2-phase algorithm for efficiently computing (plausible) positives of many standard machine learning models.
  arXiv  Detail & Related papers  (2020-10-06T11:50:28Z)
• Explanations of Black-Box Model Predictions by Contextual Importance and Utility [1.7188280334580195]
  We present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation.
  arXiv  Detail & Related papers  (2020-05-30T06:49:50Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.