ViCE: Visual Counterfactual Explanations for Machine Learning Models

Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning

Counterfactual explanations, which deal with "why not?" scenarios, can provide insightful explanations to an AI agent's behavior. In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning. Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action. We also eva…

Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence

In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showing confidence scores in human-agent interaction systems can help build trust between humans and AI systems. However, most existing research only used the confidence score as a form of communication, and we still lack ways to explain why the algorithm is confident. This paper also presents two methods for understanding model confidence using counterfactual explanation: (1) based on counterfactual examples; and (2) base…

提升对人工智能的理解与信任：用户如何从基于区间的反事实解释中受益

Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations

本研究通过实验评估不同后验解释类型对模型理解及信任的影响，发现区间解释在提升理解和信任方面优于其他类型。

可解释性的特征：用户如何理解XAI中针对类别特征和连续特征的反事实与因果解释

Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI

该研究通过127人的对照实验发现，反事实解释在提升用户预测AI决策准确性上与因果解释相当但主观满意度更高，且用户对类别特征解释的理解优于连续特征。

GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning

With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especially for image classification tasks, many state-of-the-art tools to explain such classifiers rely on visual highlighting of important areas of the input data. Contrary, counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image in a way such that the classifier would have made a different prediction. By doing so, the users of counterfactual explanation systems are equippe…

DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable. Among various explanation techniques, counterfactual explanations have the advantages of being human-friendly and actionable -- a counterfactual explanation tells the user how to gain the desired prediction with minimal changes to the input. Besides, counterfactual explanations can also serve as efficient probes to the models' decisions. In this work, we exploit the potential of counterfactual explanati…