OCCAM discovers open-set visual concepts, estimates causal contributions via object-level interventions on black-box vision models, and induces a global concept ontology from aggregated dataset evidence.
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A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
Explanation preferences for AI privacy redaction vary systematically with domain and redaction amount; giving users their preferred styles raises trust over random or no explanations.
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
Prospective situation awareness enhancing interfaces delivered via AR HUD improve takeover performance after silent automation failures, with perceptual cues most effective at raising situational awareness and system-intent messages best at building trust.
citing papers explorer
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OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
OCCAM discovers open-set visual concepts, estimates causal contributions via object-level interventions on black-box vision models, and induces a global concept ontology from aggregated dataset evidence.
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From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
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Exploring the Interaction of Explanation Styles, Context, and Trust of AI Privacy Redaction in AI-mediated Interactions
Explanation preferences for AI privacy redaction vary systematically with domain and redaction amount; giving users their preferred styles raises trust over random or no explanations.
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CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
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From Awareness to Intent: Mitigating Silent Driving System Failures through Prospective Situation Awareness Enhancing Interfaces
Prospective situation awareness enhancing interfaces delivered via AR HUD improve takeover performance after silent automation failures, with perceptual cues most effective at raising situational awareness and system-intent messages best at building trust.