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Testing machine learning explanation methods
There are many methods for explaining why a machine learning model produces a given output in response to a given input. The relative merits of these...
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Benchmarking and survey of explanation methods for black box models
The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that...
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Which Explanation Should be Selected: A Method Agnostic Model Class Reliance Explanation for Model and Explanation Multiplicity
Feature importance techniques offer valuable insights into machine learning (ML) models by conducting quantitative assessments of the individual...
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Preventing deception with explanation methods using focused sampling
Machine learning models are used in many sensitive areas where, besides predictive accuracy, their comprehensibility is also essential....
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Evaluating Explanation Methods for Multivariate Time Series Classification
Multivariate time series classification is an important computational task arising in applications where data is recorded over time and over multiple...
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DExT: Detector Explanation Toolkit
State-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented...
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Explainability Metrics and Properties for Counterfactual Explanation Methods
The increasing application of Explainable AI (XAI) methods to enhance the transparency and trustworthiness of AI systems designates the need to...
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Selecting Explanation Methods for Intelligent IoT Systems: A Case-Based Reasoning Approach
The increasing complexity of intelligent systems in the Internet of Things (IoT) domain makes it essential to explain their behavior and...
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Explanation of Results
In this chapter, we specify the business requirements and propose the solution concept for explainability. To build trust between human and machine,...
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Explanation and Agency: exploring the normative-epistemic landscape of the “Right to Explanation”
A large part of the explainable AI literature focuses on what explanations are in general, what algorithmic explainability is more specifically, and...
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A Meta Survey of Quality Evaluation Criteria in Explanation Methods
The evaluation of explanation methods has become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque...
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MEGAN: Multi-explanation Graph Attention Network
We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge...
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Explanation Paradigms Leveraging Analytic Intuition (ExPLAIn)
In this paper, we present the envisioned style and scope of the new topic “Explanation Paradigms Leveraging Analytic Intuition” (ExPLAIn) with the...
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Self-explanation prompts in video learning: an optimization study
The self-explanation strategy motivates learners to actively select and integrate information, thereby fostering meaningful learning. To generate...
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Hybrid Prompt Recommendation Explanation Generation combined with Graph Encoder
Recommendation systems have been effectively utilized in various fields, but their internal decision-making methods are still largely unknown. This...
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MANet: Mixed Attention Network for Visual Explanation
Various visual explanation methods, such as CAM and Grad-CAM, have been proposed to visualize and interpret predictions made by CNNs. Recent efforts...
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Forest GUMP: a tool for verification and explanation
In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for verification and precise explanation of Random forests....
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Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods
Model-agnostic methods in eXplainable Artificial Intelligence (XAI) propose isolating the explanation system from the AI model architecture,...
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Explanation-based data-free model extraction attacks
Deep learning (DL) has dramatically pushed the previous limits of various tasks, ranging from computer vision to natural language processing. Despite...
