问责机制
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
问责是指对 AI 系统产生的影响承担责任。
问责通常涉及透明度，即共享有关系统行为和组织流程的信息，这可能包括记录和共享模型和数据集的创建、训练和评估方式。以下网站介绍了两种有价值的问责文档模式：
可解释性是可信度的另一个维度，涉及对机器学习模型决策的理解，人类能够识别导致预测结果的特征。此外，可解释性是指能够以人类能够理解的方式解释模型的自动化决策。
如需详细了解如何赢得用户对 AI 系统的信任，请参阅“人与 AI”指南的可解释性 + 信任部分。您还可以查看 Google 的可解释性资源，了解真实示例和最佳实践。
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2025-07-27。
[null,null,["最后更新时间 (UTC)：2025-07-27。"],[[["\u003cp\u003eAccountability in AI involves taking ownership for the effects of a system, often achieved through transparency about the system's development and behavior.\u003c/p\u003e\n"],["\u003cp\u003eTransparency can be enhanced using documentation practices like Model Cards and Data Cards, which provide information about models and datasets.\u003c/p\u003e\n"],["\u003cp\u003eInterpretability and explainability are crucial aspects of accountability, enabling understanding of model decisions and providing human-understandable explanations for automated actions.\u003c/p\u003e\n"],["\u003cp\u003eFostering user trust in AI systems requires focusing on explainability and transparency, with further resources available in Google's Responsible AI Practices and Explainability Resources.\u003c/p\u003e\n"]]],[],null,["# Accountability\n\n\u003cbr /\u003e\n\n**Accountability** means owning responsibility for the effects of an AI system.\nAccountability typically involves **transparency**, or sharing information about\nsystem behavior and organizational process, which may include documenting and\nsharing how models and datasets were created, trained, and evaluated. The\nfollowing sites explain two valuable modes of accountability documentation:\n\n- [Model Cards](https://modelcards.withgoogle.com/about)\n- [Data Cards](https://sites.research.google/datacardsplaybook/)\n\nAnother dimension of accountability is **interpretability** , which involves the\nunderstanding of ML model decisions, where humans are able to identify features\nthat lead to a prediction. Moreover, **explainability** is the ability for a\nmodel's automated decisions to be explained in a way for humans to understand.\n\nRead more about building user trust in AI systems in the [Explainability +\nTrust](https://pair.withgoogle.com/chapter/explainability-trust/) section of the\n[People + AI Guidebook](https://pair.withgoogle.com/guidebook).\nYou can also check out [Google's Explainability Resources](https://explainability.withgoogle.com/)\nfor real life examples and best practices."]]
