arXiv AI
Jul 7
Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
arXiv:2501. 05795v4 Announce Type: replace-cross Abstract: In recent years, explainability in machine learning has gained importance.
P$^2$CE: Model-Agnostic Plausible Pareto-Optimal Counterfactual Explanations
arXiv:2606. 18418v1 Announce Type: new Abstract: The increasing use of machine learning algorithms in social applications has raised concerns about fairness and transparency, leading to the development of counterfactual explanations.
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arXiv Machine Learning
Jul 1
DISCOVER: A Solver for Distributional Counterfactual Explanations
arXiv:2603. 16436v2 Announce Type: replace Abstract: Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions.
arXiv Machine Learning
Jun 10
Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models
arXiv:2606. 10347v1 Announce Type: new Abstract: Machine learning is increasingly used in critical domains, where both predictions and their associated confidence levels influence important decisions.
arXiv Machine Learning
Jul 27
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
arXiv:2607. 22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome.
arXiv AI
Jul 3
PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
arXiv:2607. 01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision.
arXiv AI
Aug 7
Counterfactual Analysis via Large Language Models
arXiv:2608. 05367v1 Announce Type: new Abstract: Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making.
arXiv AI
Jul 28
Fairness Interventions in Classification: A Study on AI Explainability
arXiv:2407. 14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds.
arXiv Machine Learning
Aug 11
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
arXiv:2608. 08674v1 Announce Type: new Abstract: In the operation of machine learning models, model update is a fundamental process that requires careful consideration of its impact on downstream decision-making.
arXiv Machine Learning
Jul 9
A Distributionally Robust Optimisation Approach to Fair Credit Scoring
arXiv:2402. 01811v2 Announce Type: replace Abstract: Credit scoring has been catalogued by the European Commission and the Executive Office of the US President as a high-risk classification task, in light of the potential harms of making loan approval decisions based on models that would be biased against certain groups.
arXiv Machine Learning
Jul 7
Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms
arXiv:2607. 05101v1 Announce Type: new Abstract: The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available to banks.
arXiv Machine Learning
Jun 2
Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
arXiv:2606. 00826v1 Announce Type: new Abstract: Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models.
arXiv AI
Jun 18
Target-confidence Recourse Using tSeTlin machines: TRUST
arXiv:2606. 18832v1 Announce Type: cross Abstract: Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems.