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12 Recommendations for Using Digital and Biometric Identity Systems

By Guest Writer on December 1, 2021

Artificial Intelligence Biometric Identity

The AI4D – Digital and Biometric Identity Systems policy paper examines issues emerging around the deployment of Artificial Intelligence (AI) in Digital and Biometric Identities (BDI) being rolled out across Africa as a central part of digital strategies to meet the UN 2030 Sustainable Development Goals (SDGs). 

SDGs Target 16.9 aims: “to provide legal identity for all, including birth registration by the year 2030″. Digital identity is also seen as key to unlocking various other development goals including universal health and education access, and financial inclusion. Digital and Biometric Identity systems present opportunities for enhancing the visibility of benefactors of social services, enhancing efficiencies in digital transacting, and a variety of other potential social and digital economy benefits. 

Risks of AI and BDI

The literature demonstrates that emergences of AI in the BDI context, however, exacerbate risks already present to both fields that arise from the centrality of personal data, mass collected and analysed, within their systems. And there are broader risks – some of which arise from the centrality of identity, some of which arise from the nature of AI, and some due to the combination of both.

These include for instance, exclusion from systems due to bias or inefficiencies; lack of accountability given stakeholder relationships and the nature of the technologies themselves; heightened risks for surveillance or monitoring; improper delegation of functions; and different ramifications of technology dependencies.

These challenges might be directly addressed by different forms of policy solutions, which specifically advance forms of transparency mechanism, human rights and other legal instruments, and/or design solutions. 

AI & BDI Case Studies

This paper draws on two cases studies – one in Ghana involving facial recognition software, and another in South Africa involving natural language processing – to add depth to these background findings on the complexities of BDI systems and AI in Africa.

Within conversations on BDI, there are two key forms of digital identity: foundational digital identity is associated with foundational public sector functions, such as national and civil registration systems, whilst functional digital identity systems are those decentralised identity systems for specific sectors or use cases.

Both case studies emerge as largely functional examples of digital identity projects, with only tangential relationships to foundational identity systems. This divergence, however, is an important finding for considering the potential environment in which the AI aspects of BDI will emerge. 

Ghana: Facial Recognition Technology

In Ghana, BACE-API has been launched as a form of facial recognition technology specifically trained to identify African faces.

The case study demonstrates that, though the technology is framed as having development goals, the ability of the technology to combat financial exclusion is questionable – since it does not seek to deal with the structural impediment underpinning that exclusion.

Additionally, the challenges in opacity within private sector interventions make it immensely challenging to identify specific harms and mitigate risks. African solutions to African problems will first have to deal with the challenges within the innovation environment, variability in the policy and regulatory environment, and then directly tackle the digital inequalities that mark that context.

Exploring a strong business case on top of those preliminary hurdles then becomes the goal for initiatives like BACE-API. 

South Africa: Natural Language Processing

In South Africa, GovChat (also a private sector company) has been launched largely as a communications platform for connecting government and citizens. Apparently enhanced by natural language processing AI, one iteration of the product collects identity information for helping to process social distress-relief grants.

It does not collect biometric data, but does collect national identity numbers. Nevertheless, the historical context applicable to the case study on biometric data and foundational identity has helped unpack dimensions of opportunities and risks within the AI and data environment.

Like Ghana, this project was more reflection of functional identity than foundational identity, but demonstrated some of challenges in trying to create pivoting business models in a context of high risk personal data. Importantly too, competition-related disputes between GovChat and WhatsApp provide a vital highlight of how competition regulation – and its domestic enforcement – might play out as a tool for combatting the global technological domination of certain firms. 

AI & BDI Recommendations

While the actual use of AI technologies may not be particularly advanced in the BDI context, there are historical lessons to be considered for future policy interventions for the African region, with social, political, and historical aspects of identity being central to understanding the technological dimensions of AI. Both case studies, importantly, demonstrate how a consideration of incentives across these dimensions inform policy choices; and how alternative incentives are necessary if different outcomes are desired. 

The comparison of the case studies provide insight across themes relating to:

  • global governance;
  • digital hegemonies and public-private intersections;
  • foundational digital identity;
  • the digital economy and innovation environments;
  • AI and its role in relation to visibility;
  • the centrality and importance of transparency;
  • strategies for addressing risks. 

These case studies thus result in the following recommendations: 

For Future Research 

  • In terms of broader future research, the case studies raise the importance of creating a research framework or methodology for helping to define how functional and foundational do and do not correspond. 
  • In terms of future case studies in this area, significant energy should be placed on outlining the actual data processing practices that underscore biometric and digital identity technologies. 
  • In terms of policy intervention areas, guidelines for the institution of socio-economic risk assessments of biometric and digital identity projects should be outlined. 
  • Identity projects arise within a particular social and political history of exclusion for many African populations. This will need to influence what we consider useful interventions to be, but also means it will be a reality that a significant area of AI technology will relate to identity authentication processes in the near future. Creating norms and standards for these kinds of activities should be a research priority. 
  • As more AI technologies are developed, a strong focus in the research should be considering the specific type of AI technologies being implemented and the specific types of data underpinning it – risk assessments should be technologically specific. 

For Policymakers 

  • Data governance frameworks are a priority foundation for the implementation of biometric and digital identity programmes. 
  • Foundational identity projects will need to constructively coalesce with functional identities in order to reap the benefits of good AI. 
  • All policies must be established within a considered analysis of the full extent of intersecting digital inequalities. 

For Lawmakers 

  • Regulatory interventions should consider the extension of transparency mechanisms in the context of biometric and digital identity, in particular. 
  • The expansion of social obligations for assuring good AI must be assured given the role of the private sector in the delivery of public goods and services. 

For Technologists 

  • The implementation of socially focused identity projects should remain authentically connected to their public good purpose. 
  • Socio-economic risk assessments should be implemented prior to, and post, the implementation of biometric and digital identity AI technologies. 

A lightly edited executive summary of AI4D – Digital and Biometric Identity Systems by Gabriella Razzano, Senior Research Fellow, Research ICT Africa

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