Contextual utility theory integrates context-sensitive factors into utility-based decision-making models. It stresses the importance of understanding individual decision-makers' preferences, values, and beliefs and the situational factors that affect them. Contextual utility theory benefits explainable AI. First, it can improve transparency and understanding of how AI systems affect decision-making. It can reveal AI model biases and limitations by considering personal preferences and context. Second, contextual utility theory can make AI systems more personalized and adaptable to users and stakeholders. AI systems can better meet user needs and values by incorporating demographic and cultural data. Finally, contextual utility theory promotes ethical AI development and social responsibility. AI developers can create ethical systems that benefit society by considering contextual factors like societal norms and values. This work, demonstrates how contextual utility theory can improve AI system transparency, personalization, and ethics, benefiting both users and developers.

翻译：情境效用理论将情境敏感因素整合到基于效用的决策模型中，强调理解个体决策者的偏好、价值观和信念及其受情境因素影响的重要性。情境效用理论对可解释人工智能具有以下裨益：第一，它能提升AI系统影响决策过程的透明度与理解度；通过考量个人偏好与情境因素，可揭示AI模型的偏见与局限性。第二，情境效用理论能使AI系统更个性化地适应使用者与利益相关者——通过纳入人口统计与文化数据，AI系统能更好地契合用户需求与价值观。最终，情境效用理论促进伦理AI开发与社会责任——通过考量社会规范与价值观等情境因素，AI开发者可构建兼具伦理价值且造福社会的系统。本研究展示了情境效用理论如何提升AI系统的透明度、个性化程度及伦理性，为用户与开发者双方创造价值。