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Found 18 papers, 4 papers with code
Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals
Counterfactual explanations are an increasingly popular form of post hoc explanation due to their (i) applicability across problem domains, (ii) proposed legal compliance (e. g., with GDPR), and (iii) reliance on the contrastive nature of human explanation.
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ
Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems due to their psychological validity, flexibility across problem domains and proposed legal compliance.
Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation
The experiments also show that CFA is competitive with many other oversampling methods many of which are variants of SMOTE.
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions
There is an increasing body of evidence suggesting that post-hoc explanation-by- example with nearest neighbors is a promising solution for the eXplainable Artificial Intelligence (XAI) problem.
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has been paid to exploring the uncertainty of these generated explanations.
Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows
Mastitis is a billion dollar health problem for the modern dairy industry, with implications for antibiotic resistance.
Instance-based Counterfactual Explanations for Time Series Classification
In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data.
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs.
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier
This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier.
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem.
The Twin-System Approach as One Generic Solution for XAI: An Overview of ANN-CBR Twins for Explaining Deep Learning
The notion of twin systems is proposed to address the eXplainable AI (XAI) problem, where an uninterpretable black-box system is mapped to a white-box 'twin' that is more interpretable.
Plotting Markson's 'Mistress'
We present a distant reading of this work designed to complement a close reading of it by David Foster Wallace (1990).
The Unexpected Unexpected and the Expected Unexpected: How People's Conception of the Unexpected is Not That Unexpected
The answers people give when asked to 'think of the unexpected' for everyday event scenarios appear to be more expected than unexpected.
How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins
This paper surveys an approach to the XAI problem, using post-hoc explanation by example, that hinges on twinning Artificial Neural Networks (ANNs) with Case-Based Reasoning (CBR) systems, so-called ANN-CBR twins.
Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines
We present a software tool that employs state-of-the-art natural language processing (NLP) and machine learning techniques to help newspaper editors compose effective headlines for online publication.
Cognitive residues of similarity
So, target objects were first presented in a comparison task (e. g., rate the similarity of this object to another) thus, presumably, modifying some of their features before asking people to visually search for the same object in complex scenes (with distractors and camouflaged backgrounds).
Innovation networks
The tri-partite framework captures networks of ideas (Concept Level), people (Individual Level) and social structures (Social-Organizational Level) and the interactions between these levels.
Surprise: Youve got some explaining to do
For the Task variable, participants either answered comprehension questions or provided an explanation of the outcome.
