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Showing 1 to 11 of 11 for “"Counterfactual explanations"”.
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On evaluating counterfactual explanations for Machine Learning Models
… Learning – ML) μέσω αντιπαραθετικών εξηγήσεων (counterfactual explanations – CF), οι οποίες περιλαμβάνουν την πραγματοποίηση μικρών μεταβολών στα δεδομένα εισόδου προκειμένου να διερευνηθούν εναλλακτικά αποτελέσματα. Αναγνωρίζοντας τη διαρκώς αυξανόμενη σημασία της αξιολόγησης της απόδοσης …
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Natural counterfactual explanations with causal awareness and actionable recourse for black-box models.
… in high-stakes decision-making domains. Counterfactual (CF) explanations, which articulate the minimal input feature alterations necessary to achieve a desired model outcome, offer a promising avenue to mitigate these issues. However, prevailing CF methods often generate explanations that …
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Supporting Human Performance with Post-hoc Explanations in Automated Decision Assistance
… promote appropriate reliance behaviors. Post-hoc explanations have emerged as a strategy to support appropriate reliance on automated decision aids based on machine learning. However, existing methods to generate post-hoc explanations often fail to demonstrate systematic effectiveness in aiding …
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Personalized Algorithmic Recourse: Towards a human-centric approach for algorithmic contestability
… individuals to challenge, appeal, or seek explanations for AI-driven decisions. Indeed, while legal frameworks, such as the EU AI Act, conceptualize contestability, they fail to describe technically or scientifically actionable methodologies for effective implementation. In machine …
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Data-Driven Bicycle Design using Performance-Aware Deep Generative Models
… and targeted design refinement tools using counterfactual explanations. This treatise finally proposes the first Deep Generative Model that actively optimizes for realism, performance, diversity, feasibility, and target satisfaction simultaneously. The proposed model achieves sweeping …
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Multiagent Approaches to Enhance Learning and Trust in AI Systems
… it introduces Banzhaf indices to generate stable counterfactual explanations for graph neural networks and proposes voting based aggregation rules such as thresholded Borda count to defend against data poisoning in ensemble learning. Taken together, these contributions show how multiagent …
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Logic, Learning, and Explanation: Theoretical and Applied Perspectives on Machine Reasoning
… COMRECGC, a novel method for producing global counterfactual explanations through common recourse: minimal, interpretable sets of graph modifications that reliably flip predictions across a dataset. A second paper in this section presents LOGIC, a framework that combines GNN embeddings with …
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An algorithm must be seen to be believed : right to an explanation of automated decision-making in the GDPR
… data controllers are required to provide explanations, as well as requirements to the content, form and timing of the information. Further this thesis discusses how data controllers can comply with this obligation. In summary, data controllers are required to provide an explanation …
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Reliable Anomaly Detection with Explanation and Feedback
… AR-Pro, an explanation module that generates counterfactual explanations and suggests potential rectifications in a domain-agnostic manner. Third, we develop a feedback module that incorporates human input to incrementally update anomaly detection models, helping maintain tight performance …
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Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making
… of generating realistic, manifold-aligned counterfactual explanations. To address this problem, we present a MIP formulation where we explicitly enforce manifold alignment by reformulating the highly nonlinear Local Outlier Factor (LOF) metric as a set of mixed-integer constraints. To …