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Following on from the publication of its Feasibility Study in December 2020, the Council of Europe's Ad Hoc Committee on Artificial Intelligence (and its subgroups) initiated efforts to formulate and draft its Possible elements of a legal framework on artificial intelligence, based on the Council of Europe's standards on human rights, democracy, and the rule of law. This document was ultimately adopted by the CAHAI plenary in December 2021. To support this effort, The Alan Turing Institute undertook a programme of research that explored the governance processes and practical tools needed to operationalise the integration of human right due diligence with the assurance of trustworthy AI innovation practices. The resulting output, Human Rights, Democracy, and the Rule of Law Assurance Framework for AI Systems: A proposal, was completed and submitted to the Council of Europe in September 2021. It presents an end-to-end approach to the assurance of AI project lifecycles that integrates context-based risk analysis and appropriate stakeholder engagement with comprehensive impact assessment, and transparent risk management, impact mitigation, and innovation assurance practices. Taken together, these interlocking processes constitute a Human Rights, Democracy and the Rule of Law Assurance Framework (HUDERAF). The HUDERAF combines the procedural requirements for principles-based human rights due diligence with the governance mechanisms needed to set up technical and socio-technical guardrails for responsible and trustworthy AI innovation practices. Its purpose is to provide an accessible and user-friendly set of mechanisms for facilitating compliance with a binding legal framework on artificial intelligence, based on the Council of Europe's standards on human rights, democracy, and the rule of law, and to ensure that AI innovation projects are carried out with appropriate levels of public accountability, transparency, and democratic governance.

The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for policymakers. It provides actionable information for policymakers who wish to implement the principles and priorities of data justice in their policymaking activities. In the first section, we introduce the nascent field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes an account of the outreach we conducted with stakeholders throughout the world in developing a nuanced and pluralistic conception of data justice and concludes with a description of the six pillars of data justice around which this guidance revolves. Depending on their contexts, potential impacts, and scale, data policymaking activities may be carried out in a way that involves stakeholder engagement. To facilitate this process, the next section provides an explainer of the Stakeholder Engagement Process and the steps it includes—preliminary horizon scanning, policy scoping and stakeholder analysis, positionality reflection, and establishing stakeholder engagement objectives and methods. Finally, the last section presents the guiding questions that will help policymakers address issues of data, digital infrastructures, and affected areas of civic, public, and private life, throughout the policy lifecycle and in accordance with the six pillars of data justice. ; This report was commissioned by the International Centre of Expertise in Montréal in collaboration with GPAI's Data Governance Working Group, and produced by the Alan Turing Institute. The research was supported, in part, by a grant from ESRC (ES/T007354/1), Towards Turing 2.0 under the EPSRC Grant EP/W037211/1, and from the public funds that make the Turing's Public Policy Programme ...

The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for impacted communities. It provides actionable information for communities who wish to implement the principles and priorities of data justice. In the first section, we introduce the nascent field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes an account of the outreach we conducted with stakeholders throughout the world in developing a nuanced and pluralistic conception of data justice and concludes with a description of the six pillars of data justice around which this guidance revolves. Depending on their contexts, potential impacts, and scale, data activities may be carried out in a way that involves stakeholder engagement. To facilitate this process, the next section provides an explainer of the Stakeholder Engagement Process and the steps it includes—preliminary horizon scanning, policy scoping and stakeholder analysis, positionality reflection, and establishing stakeholder engagement objectives and methods. This section sets out considerations relating to internal community engagement (i.e. engagement within your community) as well as approaches to engaging external stakeholders (i.e. to inform or influence external activities). Additionally, it sets out considerations to be addressed when participating in externally-led engagement processes (e.g. where communities are invited to participate in stakeholder engagement initiated by developers or policy-makers). Finally, the last section presents the guiding questions that will help communities to address issues of data, digital infrastructures, and affected areas of civic, public, and private life, in relation to past, present and future dimensions of ...

The idea of "data justice" is of recent academic vintage. It has arisen over the past decade in Anglo-European research institutions as an attempt to bring together a critique of the power dynamics that underlie accelerating trends of datafication with a normative commitment to the principles of social justice—a commitment to the achievement of a society that is equitable, fair, and capable of confronting the root causes of injustice.However, despite the seeming novelty of such a data justice pedigree, this joining up of the critique of the power imbalances that have shaped the digital and "big data" revolutions with a commitment to social equity and constructive societal transformation has a deeper historical, and more geographically diverse, provenance. As the stories of the data justice initiatives, activism, and advocacy contained in this volume well evidence, practices of data justice across the globe have, in fact, largely preceded the elaboration and crystallisation of the idea of data justice in contemporary academic discourse. We have organised the stories contained in this repository into two groups. The first group, 'Challenges to Data Justice: Stories of Data Discrimination and Inequity", poses the question: What are the sorts of problems and challenges to which data justice practitioners are responding? This section is intended to orient the reader to the range of empirical problems faced by data justice researchers and practitioners the world over. We have provided examples of data practices that have been criticised as posing risks of moral injury and that have been identified as leading to inequitable or discriminatory outcomes. Case studies include a national ID card that serves as a government payment system in Venezuela, a courier service/digital technology company in Colombia, and a digital registry of 'rights, tenancy, and crops' in India. The second group, 'Transformational Stories of Data Justice: Initiatives, Activism, and Advocacy', poses the questions: What do responses to the range of ...

The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for developers and organisations, which are producing, procuring, or using data-intensive technologies. It provides actionable information for those who wish to implement the principles and priorities of data justice in their data practices and within their data innovation ecosystems. In the first section, we introduce the nascent field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes an account of the outreach we conducted with stakeholders throughout the world in developing a nuanced and pluralistic conception of data justice and concludes with a description of the six pillars of data justice around which this guidance revolves. Next, to support developers in designing, developing, and deploying responsible and equitable data-intensive and AI/ML systems, we outline the AI/ML project lifecycle through a sociotechnical lens, walking the reader through each phase and noting the ethics and governance considerations that should occur at each step of the way. This portion of the guide is intended to provide a background picture of the different stages of the lifecycle and to show how the data justice pillars can be woven into the stages and their respective sociotechnical considerations. To support the operationalisation data justice throughout the entirety of the AI/ML lifecycle and within data innovation ecosystems, we then present five overarching principles of responsible, equitable, and trustworthy data research and innovation practices, the SAFE-D principles—Safety, Accountability, Fairness, Explainability, and Data Quality, Integrity, Protection, and Privacy. These principles support and underwrite the ...

This resource is divided into two primary sections. The first is an annotated bibliography of works related to our integrated literature review on data justice, and the second is a table of organisations conducting data justice or data justice adjacent work. The annotated bibliography contains works relevant to each theme of the integrated literature review which is an accompanying document to this resource. Within each theme and sub-theme key works as well as summaries are provided to direct the reader to additional readings about the topics. This annotated bibliography is not an exhaustive resource, but rather meant to serve as a starting point for learning more about these topics. The table of organisations contains information about organisations conducting data justice or adjacent data justice work across the globe. To ensure the inclusion of a diverse set of organisations from across the globe and across relevant stakeholder groups, the team adopted a three-pronged approach to the identification of organisations. First, recommendations were taken from our existing advisory board members whose expertise on data justice within their regions of operation allowed them to identify organisations which might have been missed. Second, existing networks were examined to identify small organisations working at the intersection of datafication and social justice. This included the Association of Progressive Communications whose aim is 'empowering and supporting people working for peace, human rights, development and protection of the environment, through the strategic use of information and technologies and Privacy International who aim 'to protect democracy, defend people's dignity, and demand accountability from institutions who breach public trust'. Third, through active research and cascading search, additional organisations were identified based on prior work on datafication and social justice, previous experience of stakeholder engagement, and strong networks among relevant stakeholder groups. The table serves ...

Motivated by the extensive documented disparate harms of artificial intelligence (AI), many recent practitioner-facing reflective tools have been created to promote responsible AI development. However, the use of such tools internally by technology development firms addresses responsible AI as an issue of closed-door compliance rather than a matter of public concern. Recent advocate and activist efforts intervene in AI as a public policy problem, inciting a growing number of cities to pass bans or other ordinances on AI and surveillance technologies. In support of this broader ecology of political actors, we present a set of reflective tools intended to increase public participation in technology advocacy for AI policy action. To this end, the Algorithmic Equity Toolkit (the AEKit) provides a practical policy-facing definition of AI, a flowchart for assessing technologies against that definition, a worksheet for decomposing AI systems into constituent parts, and a list of probing questions that can be posed to vendors, policy-makers, or government agencies. The AEKit carries an action-orientation towards political encounters between community groups in the public and their representatives, opening up the work of AI reflection and remediation to multiple points of intervention. Unlike current reflective tools available to practitioners, our toolkit carries with it a politics of community participation and activism.