Titlebook: Explainable Artificial Intelligence; First World Conferen Luca Longo Conference proceedings 2023 The Editor(s) (if applicable) and The Auth

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Lacerate

Natural Example-Based Explainability: A Surveyng models. While saliency maps have stolen the show for the last few years in the XAI field, their ability to reflect models’ internal processes has been questioned. Although less in the spotlight, example-based XAI methods have continued to improve. It encompasses methods that use examples as expla

白楊魚

Presbyopia

Contrastive Visual Explanations for?Reinforcement Learning via?Counterfactual Rewardsive explanation framework for reinforcement learning (RL) based on comparing learned policies under actual environmental rewards vs. hypothetical (counterfactual) rewards. The framework provides policy-level explanations by accessing learned Q-functions and identifying intersecting critical states.

STALE

共同確定為確

共同確定為確

A Novel Architecture for?Robust Explainable AI Approaches in?Critical Object Detection Scenarios Basritical situations. We provide a novel approach focusing on potential edge cases in order to address those specific situations which are often most the hardest part in bringing machine learning models into production within the aforementioned scenarios. We improve upon existing explainable artificia

aspect

Jocose

exorbitant

The Duet of?Representations and?How Explanations Exacerbate Itwith the human’s prior belief. Explanations can direct the human’s attention to the conflicting feature and away from other relevant features. This leads to causal overattribution and may adversely affect the human’s information processing. In a field experiment we implemented an XGBoost-trained mod