iREAL: Inclusive Requirements Elicitation for AI in Libraries to Support Respectful Management of Indigenous Knowledges

iREAL：图书馆人工智能的包容性需求获取，支持对本土知识的尊重管理

• 批准号: AH/Z505638/1
• 负责人: Paul Gooding
• 金额: $ 26.32万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=AH%2FZ505638%2F1
• 关键词: iREAL; Inclusive; Requirements; Elicitation; AI

iREAL will develop a model for responsible AI systems development in libraries seeking to include knowledge from Indigenous communities, specifically Aboriginal and Torres Strait Islander communities in Australia. Globally, Libraries hold collections extracted from "source communities"; a term commonly used in UK cultural heritage scholarship to refer to Indigenous communities. Existing research has addressed collaboration with source communities in digitisation and preservation of Indigenous knowledges (e.g. Boamah and Liew, 2016), adaptation of professional practices including cataloguing (Lawther, 2023), and engagement with the "methodological, ethical and practical changes" required to accommodate multiple perspectives in national collections (Pringle et al., 2022). Roke and Tillman (2022) have developed pragmatic principles for engaging with communities to develop, describe and use source community collections and knowledges.Indigenous researchers have proposed guidelines for "Indigenous data governance" (Maiam nayri Wingara, & Australian Indigenous Governance Institute 2018) and Indigenous-centred AI design (Lewis, 2020), but there has been little application of these principles, or Indigenous community engagement, in libraries. We will therefore address the need for clear guidance on responsible AI systems assessment and development that embeds Indigenous rights and perspectives into the principles of "respect for human autonomy, prevention of harm, fairness and explicability" that are the ethical basis of trustworthy AI (High Level Expert Group on Artificial Intelligence, 2019). This guidance must be scalable, as project-funded initiatives are argued to poorly serve relationships between stakeholders and institutions, and sustainable, as the challenge of dealing with collections that are colonial and extractive in nature affects institutions of all sizes. We will intervene by working with Indigenous communities to scope the knowledge by which libraries, and other Galleries, Libraries, Archives and Museums (GLAM) can: reconnect Indigenous communities with their knowledge and heritage; foster relationships between information management professionals (IMPs), research software engineers (RSEs), and Indigenous communities; and co-develop or critique AI systems to balance the objectives of libraries with the rights and needs of Indigenous communities.We will apply this knowledge to create an actionable, pragmatic and scalable model for source community engagement in the assessment and development of AI systems via the requirements elicitation process. To achieve this, we will address the following objectives:Develop a deeper understanding of Indigenous rights and data governance, and its application to AI systems in libraries in Australia and the United Kingdom.Equip IMPS, RSEs, Indigenous researchers, and Indigenous communities with knowledge of the challenges in deploying Indigenous data within AI systems.Propose a preliminary model for inclusive requirements elicitation in AI systems assessment or development for libraries, based initially upon collections from Aboriginal and Torres Strait Islander communities.We will invite participants from a broad range of perspectives and countries to collaborate in pathfinder workshops, to define how responsible AI systems might be critiqued and created including the knowledge and data of Indigenous communities. Given the widespread global distribution of these collections, our distinctive contribution is to develop a requirements elicitation process that can be applied when considering whether, and how, to use source community data in library-developed or adopted systems.

IREAL将开发一个模型，用于在澳大利亚澳大利亚的原住民社区，特别是原住民和托雷斯海峡岛民社区的知识的图书馆中负责的AI系统开发模型。在全球范围内，图书馆持有从“来源社区”中提取的藏品；英国文化遗产奖学金中常用的一个术语，指的是土著社区。现有的研究已经解决了与原始社区的合作，以数字化和保存土著知识（例如Boamah and Liew，2016年），适应包括分类（Lawther，2023）在内的专业实践，以及“方法论，道德和实践”的参与，以适应国家集团在国家集团中的多个观点（Pringle et pringle et a pringle等）。 Roke and Tillman (2022) have developed pragmatic principles for engaging with communities to develop, describe and use source community collections and knowledges.Indigenous researchers have proposed guidelines for "Indigenous data governance" (Maiam nayri Wingara, & Australian Indigenous Governance Institute 2018) and Indigenous-centred AI design (Lewis, 2020), but there has been little application of these principles, or图书馆中的土著社区参与。因此，我们将解决对负责任的AI系统评估和发展的明确指导，将土著权利和观点嵌入到“尊重人类自主权，预防伤害，公平性和明确性”的原则中，这些原则是可信赖的AI（人工智能高级专家小组，2019年，高级专家群体）的道德基础。该指南必须是可扩展的，因为由项目资助的举措争论不良的利益相关者和机构之间的关系以及可持续的关系，因为应对本质上殖民地和挖掘的收藏的挑战会影响各种规模的机构。我们将通过与土著社区合作来介绍图书馆以及其他画廊，图书馆，档案和博物馆（Glam）可以：重新与他们的知识和遗产重新连接土著社区的知识；促进信息管理专业人员（IMP），研究软件工程师（RSE）和土著社区之间的关系；以及共同开发或批评AI系统，以平衡图书馆的目标与土著社区的权利和需求。我们将应用此知识来创建一个可行，务实和可扩展的模型，以通过需求激励过程在评估和开发AI系统的评估和开发中来源。为此，我们将解决以下目标：对土著权利和数据治理进行更深入的了解，及其在澳大利亚和英国的图书馆中的AI系统应用于AI系统。Equip Ipps，RSE，RSE，RSES，RSES，RSES，研究人员以及土著社区以及土著社区在AI Systems Anepose a Indipose a Indersiation Assipose Assirimentiars Assi的挑战方面的知识。最初基于原住民和托雷斯海峡岛民社区的收藏的图书馆。我们将邀请参与者从广泛的角度和国家 /地区合作在探路者研讨会上进行合作，以定义如何批评和创建负责任的AI系统，包括土著社区的知识和数据。鉴于这些藏品的全球分布广泛，我们独特的贡献是开发一个需求启发过程，在考虑是否以及如何在图书馆开发或采用的系统中使用源社区数据时可以应用。