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Building a minimal viable digital identity from digital footprints (‘MVDI’)

15th September - 15th December 2023
Image by George Prentzas
Project team
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Dr Heather Shaw

Principal Investigator

Lecturer, Lancaster University

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Dr Anita Khadka

Co-Investigator

Assistant Professor, Northeastern University

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Dr Andrew M’manga

Co-Investigator

Senior Lecturer, Bournemouth University

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Dr Carl Adams

Co-Investigator

CEO, Lead Researcher, Mobi Publishing

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Dr Yuchen Zhao

Co-Investigator

Lecturer, University of York

Summary

Our goal was to establish a foundation for future Digital Identity (DI) systems by leveraging digital footprints, with a key emphasis on trust, privacy, and security as foundational principles. We delved into the exploration of whether digital footprint data could be deemed reliable enough to serve as the basis for a digital ID that holds equal legitimacy to traditional documents like passports and birth certificates. We coined the term “minimal viable digital identity” (MVDI) to describe what little data is needed to create this digital identity.

Our approach recognises that individuals may be from backgrounds with less access to technologies and digital services. These individuals should not be disadvantaged when forming digital identities. Therefore, our focus centred on developing a minimum viable digital identity specifically tailored for migrants, refugees, and asylum seekers, utilising insights from their digital footprints. This initiative aimed to outline a DI approach that could grant them access to essential services, including healthcare and education. These individuals may have lost or thrown away their state-issued documentation. Consequently, these are the individuals who are likely to benefit the most from a digital identity that can be used in place of passports and birth certificates.

Outputs

Presentation on the DigiProPass project from the SPRITE+ Conference, June 2023.

Adams, C., Eslamnejad, M., Khadka, A., M’manga, A., Shaw, H., Zhao, Y. (2023). Auditing AI Systems: A Metadata Approach. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_22

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