iREAL: Inclusive Requirements Elicitation for AI in Libraries to Support Respectful Management of Indigenous Knowledges
Lead Research Organisation:
University of Glasgow
Department Name: School of Humanities
Abstract
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.
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.
