FRAIM: Framing Responsible AI Implementation and Management

背景人工智能技术的应用日益增多，需要在组织政策和实践中迅速演变。然而，这些快速变化往往孤立于单个组织和部门，缺乏共享的跨部门学习，以及伴随而来的负责任和道德的人工智能(RAI)的共同价值观。与此同时，RAI资源激增，但很少有资源有效应对执行共同原则方面的挑战，进一步阻碍了最佳做法的发展。当世界各地的政策制定者和行业努力指导、规范和设计RAI时，显然需要建立共同的价值观和对实践中实施和管理RAI所涉及的因素的知识。挑战框架负责任的人工智能实施和管理(FRIM)项目将汇集跨部门的观点，对组织的RAI政策和流程进行审查，以确定关键利益相关者、共享的价值观和可操作的研究需求，以建立实施和管理RAI的证据基础。我们与代表人工智能使用将显著影响人们生活的关键领域的组织建立了合作伙伴关系，包括当地政策、信息获取和文化丰富。我们与这些合作伙伴的范围划分工作将为未来实践和干预措施的发展提供坚实的基础，以实现RAI的生态系统方法，并创造性地和批判性地检查RAI的组织实施和管理。目标和目标该项目将专注于围绕使用人工智能技术的组织政策和流程，包括预先培训的基础模型以及特定于背景的机器学习，以帮助组织信息和支持决策。有了这个重点和我们合作伙伴的具体专业知识，该项目将解决两个主要目标：目标1：在组织政策和RAI实施和管理的过程中绘制关键利益相关者和价值观的网络。目标2：针对跨部门的RAI实施和管理建立更强大的证据基础的范围可操作的需求。我们将通过与我们的合作伙伴合作完成三个目标来实现这些目标：目标1：通过对RAI资源和文献进行元分析，查询和整理当前RAI话语中描述的价值观、问题和实施和管理挑战。目标2：确定关键的利益相关者、实践、通过与合作伙伴组织的员工进行探索性访谈，在组织层面上实施和管理RAI所涉及的范围和价值。目标3：通过与项目合作伙伴合作举办的范围确定研讨会，确定扩大RAI证据基础的具体计划，以便为RAI的实施、管理和政策提供信息。潜在的应用和好处本项目中的范围确定工作将为以生态系统为重点的研究和共享价值确定明确的方向和下一步步骤，以指导组织内的RAI政策和流程。通过借鉴多方利益相关者的观点并创造性地参与实践中的RAI的复杂问题，该项目将受益于：合作伙伴组织，通过分享其他组织对RAI政策和流程的方法的见解。RAI研究社区，通过基于经验的地图，为RAI在组织范围内的使用。公众，通过对人工智能实施和管理的见解，以及对他们周围世界的RAI政策和流程的创造性思考。

ContextIncreasing applications of AI technologies have necessitated rapid evolution in organisational policy and practice. However, these rapid changes have often been isolated in individual organisations and sectors, with a lack of shared cross-sectoral learning and accompanying shared values for responsible and ethical AI (RAI). Meanwhile, RAI resources have proliferated, but few effectively address the challenges in implementing shared principles, further hindering best practice development. As policymakers and industry worldwide grapple to guide, regulate, and design RAI, there is a clear need for establishing shared values and knowledge of the factors involved in implementing and managing RAI in practice.ChallengeThe Framing Responsible AI Implementation and Management (FRAIM) project will bring together cross-sector perspectives on organisational RAI policy and process to scope key stakeholders, shared values, and actionable research needs for building the evidence base on implementing and managing RAI. We have partnered with organisations representing example key areas in which AI use will significantly impact people's lives, including local policy, information access, and cultural enrichment. Our scoping work with these partners will provide a strong foundation for future development of practices and interventions to enable an ecosystem approach to RAI, and creatively and critically examine organisational implementation and management of RAI.Aims and ObjectivesThe project will focus on organisational policies and processes around using AI technologies, including pre-trained foundation models as well as context-specific machine learning, to help organise information and support decision-making. With this focus and the specific expertise of our partners, the project will address two key aims:Aim 1: Map the network of key stakeholders and values in organisational policy and process towards RAI implementation and management.Aim 2: Scope actionable needs for building a stronger evidence base around RAI implementation and management across sectors.We will achieve these aims by working with our partners to complete three objectives:Objective 1: To query and collate the values, questions, and implementation and management challenges being described in the current RAI discourse by performing a meta-analysis of RAI resources and literature.Objective 2: To identify key stakeholders, practices, and values involved in implementing and managing RAI at organisational levels by conducting exploratory interviews with staff from partner organisations.Objective 3: To scope specific plans for expanding the RAI evidence base to inform RAI implementation, management, and policy via a scoping workshop held in collaboration with project partners.Potential applications & benefitsThe scoping work in this project will establish clear directions and next steps for ecosystem-focused research and shared values to guide RAI policy and process within organisations. By drawing on multi-stakeholder perspectives and creatively engaging with the complex questions of RAI in practice, the project will benefit:Partner organisations, through shared insights from other organisations' approaches to RAI policy and process.The RAI research community, through empirically-grounded mapping of values, policies, and processes for RAI use across organisational contexts.The public, through insights into AI implementation and management and creative reflection on RAI policy and process in the world around them.