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.

Objectives

1. Identification of the acceptability/ethics of using a digital footprint as an MVDI.

2. Investigation of affordances in terms of privacy, trust and security are indicative of different digital footprints, and how can we define these.

3. Explore the current and future digital footprints that are likely to be associated with diverse demographics of migrants/refugees/asylum seekers.

4. Explore linking footprints together and verifying data from the same individual.

We also aimed to get the above insights reviewed by stakeholders during a workshop for checking/verification purposes. However, we underwent several barriers that prevented us from doing so. 1) Delay in ethical approval 2) People lacked availability to take part 3) People dropping out.

Instead, we completed this checking and verification throughout the project by reviewing each other's work and taking a cross-disciplinary perspective. We set up weekly discussions, in order to continually check and validate each other's work. We also reviewed specific use cases along the way to ensure our ideas supported these individuals. 

Activities

1. A narrative review that covered:

   • Assessment of our use case

   • Evaluation of existing DI systems

   • Inequalities of DI systems

   • Social science assessment of identity and privacy

   • Future scanning of novel and emerging technologies

   • Assessment of digital data linkage

2. The creation of taxonomies from research findings.

3. Mapping of DI affordances.

4. Applied ideas to an in-depth case study.

5. Combined the outputs of the narrative review, taxonomies, affordance dictionary and conceptual MVDI model into one academic output.

   
   

Outputs

Presentation on the MVDI 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

Presentation at the SPRITE+ 2024 Showcase:

Impact

• We have outlined a minimum viable digital identity approach, which others can adopt in their practices.

• We have provided a map of affordances that are relevant to MVDI that can be used to evaluate a DI system.

• We have provided a taxonomy of current and future digital footprints that can be used to form a MVDI.

 Our key findings include:

• MVDI is an ecosystem (multiple systems), working together to achieve a common goal.

• MVDI is context specific, as people express different parts of their identity depending on the social group/environment/situation.

• MVDI can be considered as a process, and is complex, multifaceted, and multidimensional.

• Generated insights on using affordance mapping as a methodology, which highlights the advantages of a multi-disciplinary approach.

Future work

We've undertaken important theoretical work. The next step is substantiating our MVDI approach through data collection. We're seeking future grants to develop an AI system which integrates the conceptual framework into the AI-based system.