Ethics-based AI auditing: A systematic literature review on conceptualizations of ethical principles and knowledge contributions to stakeholders
这篇系统文献综述梳理了AI审计文献中如何定义公平、透明等伦理原则，以及这些研究为开发者、公众、监管者等利益相关者提供了哪些知识贡献（如指导、方法工具、意识提升）。
This systematic literature review synthesizes the conceptualizations of ethical principles in AI auditing literature and the knowledge contributions to the stakeholders of AI auditing. We explain how the literature discusses fairness, transparency, non-maleficence, responsibility, privacy, trust, beneficence, and freedom/autonomy. Conceptualizations vary along social/technical- and process/outcome-oriented dimensions. The main stakeholders of ethics-based AI auditing are system developers and deployers, the wider public, researchers, auditors, AI system users, and regulators. AI auditing provides three types of knowledge contributions to stakeholders: 1) guidance; 2) methods, tools, and frameworks; and 3) awareness and empowerment.