Researchers, government bodies, and organizations have been repeatedly calling for a shift in the responsible AI community from general principles to tangible and operationalizable practices in mitigating the potential sociotechnical harms of AI. Frameworks like the NIST AI RMF embody an emerging consensus on recommended practices in operationalizing sociotechnical harm mitigation. However, private sector organizations currently lag far behind this emerging consensus. Implementation is sporadic and selective at best. At worst, it is ineffective and can risk serving as a misleading veneer of trustworthy processes, providing an appearance of legitimacy to substantively harmful practices. In this paper, we provide a foundation for a framework for evaluating where organizations sit relative to the emerging consensus on sociotechnical harm mitigation best practices: a flexible maturity model based on the NIST AI RMF.

翻译：研究人员、政府机构及组织一再呼吁负责任人工智能领域从笼统原则转向具体且可操作的最佳实践，以减轻人工智能潜藏的社会技术危害。以NIST AI RMF为代表的框架体系，体现了将社会技术风险缓解付诸实践方面逐渐形成的共识。然而，私营部门组织目前仍远落后于这一新兴共识。其落实情况充其量只是零散且具有选择性的实践，最糟的情况下则流于无效，甚至可能沦为可信流程的虚假表象，为实质上具有危害性的行为披上合法外衣。本文为评估组织在社会技术风险缓解最佳实践方面的共识达成度提供了框架基础——即一个基于NIST AI RMF的灵活成熟度模型。