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Found 8 papers, 1 papers with code
Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning
We present a text mining system to support the exploration of large volumes of text detailing the findings of government inquiries.
Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI
Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e. g. a customer refused a loan might be told: If you asked for a loan with a shorter term, it would have been approved).
Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise
Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i. e., whether they differ in their expertise).
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI
It is also unknown whether counterfactual explanations are equally effective for categorical as for continuous features, although current methods assume they do.
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR).
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future
Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet.
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques
In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI).
A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations
Counterfactual explanations provide a potentially significant solution to the Explainable AI (XAI) problem, but good, native counterfactuals have been shown to rarely occur in most datasets.
