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Found 16 papers, 3 papers with code
Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics
Artificial Intelligence (AI) finds widespread applications across various domains, sparking concerns about fairness in its deployment.
Tell Me a Story! Narrative-Driven XAI with Large Language Models
Data scientists primarily see the value of SHAPstories in communicating explanations to a general audience, with 92% of data scientists indicating that it will contribute to the ease and confidence of nonspecialists in understanding AI predictions.
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.
Calculating and Visualizing Counterfactual Feature Importance Values
Using these importance values, we additionally introduce three chart types to visualize the counterfactual explanations: (a) the Greedy chart, which shows a greedy sequential path for prediction score increase up to predicted class change, (b) the CounterShapley chart, depicting its respective score in a simple and one-dimensional chart, and finally (c) the Constellation chart, which shows all possible combinations of feature changes, and their impact on the model's prediction score.
Unveiling the Potential of Counterfactuals Explanations in Employability
In eXplainable Artificial Intelligence (XAI), counterfactual explanations are known to give simple, short, and comprehensible justifications for complex model decisions.
Disagreement amongst counterfactual explanations: How transparency can be deceptive
Even though in some contexts multiple possible explanations are beneficial, there are circumstances where diversity amongst counterfactual explanations results in a potential disagreement problem among stakeholders.
The privacy issue of counterfactual explanations: explanation linkage attacks
Black-box machine learning models are being used in more and more high-stakes domains, which creates a growing need for Explainable AI (XAI).
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.
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness.
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
This benchmarking study and framework can help practitioners in determining which technique and building blocks most suit their context, and can help researchers in the design and evaluation of current and future counterfactual generation algorithms.
NICE: An Algorithm for Nearest Instance Counterfactual Explanations
We propose four versions of NICE, one without optimization and, three which optimize the explanations for one of the following properties: sparsity, proximity or plausibility.
Explainable Image Classification with Evidence Counterfactual
In this paper, SEDC is introduced as a model-agnostic instance-level explanation method for image classification to obtain visual counterfactual explanations.
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.
Dance Hit Song Prediction
Record companies invest billions of dollars in new talent around the globe each year.
Explaining Classification Models Built on High-Dimensional Sparse Data
We evaluate these alternatives in terms of explanation "bang for the buck,", i. e., how many examples' inferences are explained for a given number of features listed.
