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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2501. 05795v4 Announce Type: replace-cross Abstract: In recent years, explainability in machine learning has gained importance.
arXiv:2603. 16436v2 Announce Type: replace Abstract: Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions.
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: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: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:2608. 05367v1 Announce Type: new Abstract: Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making.