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Found 3 papers, 1 papers with code
Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data
In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints.
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.
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