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Found 6 papers, 1 papers with code
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem
Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life, increasing the need to explain these decisions and to make sure that they are aligned with how we want the decision to be made.
Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data
Machine learning models on behavioral and textual data can result in highly accurate prediction models, but are often very difficult to interpret.
Counterfactual Explanation Algorithms for Behavioral and Textual Data
This study aligns the recently proposed Linear Interpretable Model-agnostic Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of counterfactual explanations, and empirically benchmarks their effectiveness and efficiency against SEDC using a collection of 13 data sets.
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data.
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease
The core components are: 1) code to automatically convert the full ADNI database into BIDS format; 2) a modular architecture based on Nipype in order to easily plug-in different classification and feature extraction tools; 3) feature extraction pipelines for MRI and PET data; 4) baseline classification approaches for unimodal and multimodal features.
Link Discovery using Graph Feature Tracking
We consider the problem of discovering links of an evolving undirected graph given a series of past snapshots of that graph.
