Data Justice in Practice: A Guide for Developers

98 Pages Posted: 20 Apr 2022

See all articles by David Leslie

David Leslie

The Alan Turing Institute

Michael Katell

The Alan Turing Institute Public Policy Programme

Mhairi Aitken

The Alan Turing Institute

Jatinder Singh

University of Cambridge -- Dept. Computer Science & Technology (Computer Laboratory)

Morgan Briggs

The Alan Turing Institute

Rosamund Powell

affiliation not provided to SSRN

Cami Rincon

The Alan Turing Institute

Antonella Perini

The Alan Turing Institute

Smera Jayadeva

The Alan Turing Institute

Christopher Burr

The Alan Turing Institute; University of Oxford - Oxford Internet Institute

Date Written: April 9, 2022

Abstract

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 advancement of data justice within research and innovation practices. We elaborate upon them as high-level goals that are then followed by further specification through the presentation of additional properties, which are to be established in either the project or the system to ensure these goals are reached.

Depending on their contexts, potential impacts, and scale, data innovation activities should be carried out in a way that involves different degrees of 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, project scoping and stakeholder analysis, positionality reflection, and establishing stakeholder engagement objectives and methods.

Finally, the last section presents guiding questions that will help developers both address data justice issues throughout the AI/ML lifecycle and engage in reflective innovation practices that ensure the design, development, and deployment of responsible and equitable data-intensive and AI/ML systems. This is done by presenting questions related to both the six pillars of data justice and the SAFE-D principles introduced previously.

Keywords: data justice, digital rights, data ethics, AI ethics, social justice, power, access, equity, participation, knowledge, data power, digital infrastructure, human rights, data colonialism, decolonial AI, economic justice, data feminism, design justice, pluriverse, post-development theory

Suggested Citation

Leslie, David and Katell, Michael and Aitken, Mhairi and Singh, Jatinder and Briggs, Morgan and Powell, Rosamund and Rincon, Cami and Perini, Antonella and Jayadeva, Smera and Burr, Christopher and Burr, Christopher, Data Justice in Practice: A Guide for Developers (April 9, 2022). Available at SSRN: https://ssrn.com/abstract=4080058 or http://dx.doi.org/10.2139/ssrn.4080058

David Leslie (Contact Author)

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

HOME PAGE: http://https://www.turing.ac.uk/people/researchers/david-leslie

Michael Katell

The Alan Turing Institute Public Policy Programme ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

Mhairi Aitken

The Alan Turing Institute ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

Jatinder Singh

University of Cambridge -- Dept. Computer Science & Technology (Computer Laboratory) ( email )

15 JJ Thomson Avenue
William Gates Building
Cambridge, CB3 0FD
United Kingdom

Morgan Briggs

The Alan Turing Institute ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

Rosamund Powell

affiliation not provided to SSRN

Cami Rincon

The Alan Turing Institute ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

Antonella Perini

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

Smera Jayadeva

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

Christopher Burr

University of Oxford - Oxford Internet Institute ( email )

1 St. Giles
University of Oxford
Oxford OX1 3PG Oxfordshire, Oxfordshire OX1 3JS
United Kingdom

The Alan Turing Institute ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

HOME PAGE: http://https://chrisdburr.com

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