Introducing biometric identity verification into micro-mobility services: an acceptability analysis based on the MADRAS project

The MADRAS project develops a product for the persons’ authentication market, which helps demonstrate a materials-driven improvement of OLAE devices. In particular, a biometric photosensor is to be integrated into motorbikes used for micro-mobility services offered by the project partner Cooltra.

This device expects to show a step forward towards digitisation and Internet of Things for its contribution to the sustainable deployment of smart products for consumer use. Validation activities conducted related to such integration processes go beyond analysing technical fitness (i.e. material resistance and conductivity) and environmental impact, but also aim to gather relevant information about the system’s acceptability, usability and overall user perception.

This effort ensures that socio-technical and desirability aspects associated with using sensitive information, including both fingerprints for identity verification and heart rate for anti-spoofing, are properly weighted while potential risk mitigation protocols are considered during technological development.

Biometrics refers to the automated or semi-automated use of physiological or behavioural characteristics to verify or determine someone’s identity. The most commonly used biometric methods are facial or iris scans, voice recognition and fingerprinting. Biometric systems can operate in either verification or identification mode.

While verification (one to one) is the process of comparing an individual’s biometric to a pre-existing template to confirm their identity, identification (one to many) is the process of comparing an individual biometric data to all templates in a database to determine their identity.

When analysing the social implications of the MADRAS reader, we focus on technological acceptability. The Unified Theory of Acceptance and Use of Technology (UTAUT) suggests a collection of factors influencing the adoption and use of technologies with main variables explaining technology acceptability.

Firstly, performance expectancy (perceived usefulness) concerns the degree to which someone assumes that using the system will help to earn improvements in a given task. Secondly, effort expectancy refers to the associated level of ease of use. Thirdly, social influence indicates to what extent a person perceives that significant others believe they should use the new system. And finally, the so-called facilitating conditions refer to the availability of organisational and technical instruments to support the use of the system.

Implementing biometric technology is facing new challenges with the emergence of “micro-mobility” services in cities, such as e-scooters. These micro-mobility services, also including e-bikes, electric skateboards, and shared bicycles, have expanded in recent years to offer swift travels over short distances.

The smooth and safe pick up of these transport systems is a crucial goal so they can adequately contribute to facilitating urban mobility and potentially reduce the environmental impact fostered by cars. Experiences include the testing (2020) of face and document recognition technology for users’ identity and age verification in the case of the Amsterdam-based e-scooter company Dott. Through anti-spoofing technology based on liveness detection, the Dott app allows riders to provide their majority age proof by submitting a selfie and a photo of an official government ID.

The literature examining the use of biometrics with authentication purposes in public transport has shown that these protocols have a high level of acceptability among users and end users. While it is worth mentioning that public opinion on this matter is mediated by the role of big tech companies in promoting the use of these technologies, this positive approach seems to depend on the usability of the system at hand, age, gender and other demographic characteristics of users.

To understand users’ perceptions regarding the incorporation of biometrics for the purpose of identity verification within the Cooltra scooter service, a sample of 112 typical and current users of Cooltra e-scooters were surveyed between November 2022 and January 2023. With over 200.000 users targeted in Spain, the survey presents an 80% confidence level with a 6% margin of error.

In the sample, 20.69% of users identified themselves as women while the majority, 71.55%, identified themselves as men. Regarding age distribution, the 112 respondents belonged to  five age categories, 18-30 (25%), 31-40 (29.31%), 41-50 (25.86%), 51-65 (12.07%) and 9 individuals did not disclose their age.

Users were asked about their perception of the need for integrating biometric identification to secure the e-scooters service and make their identity verification smoother. While 11% and 18% of respondents strongly agreed or agreed, 21% and 26% of them strongly disagreed or disagreed with the statement: “I believe that the use of the fingerprint system is necessary for the verification of identity and the security of the service”. This suggests that alternative identity verification and security measures might need to be considered to address the concerns of respondents.

Moreover, when it comes to the perceived burden of using biometrics (Figure 1), while men tend to view the integration of this technology in micro-mobility as a relatively minor burden for users, women tend to disagree with this viewpoint. Despite the small sample size of women included in the study, the findings suggest a potentially reduced level of acceptability of biometrics in this particular context.

Biometric technologies, which involve the collection and automatic process of a considerable amount of sensitive information (e.g. fingerprinting), are increasingly integrated into mobility services with several purposes (i.e. payment security checks, pick-up identity verification processes, anti-spoofing, etc.).

Such development entails several legal, technical and social challenges. Integrating users’ perceptions into the design of new biometric services is vital for ensuring their feasibility and compliance with their data projection and AI-related rights.

The MADRAS survey shows a mix of technological optimism and pessimism regarding the capacity of biometric technology to identify them and make the motorbikes’ pick-up process smoother, coupled with a sceptical attitude toward protecting privacy.

Furthermore, differences in users’ perceptions depending on age and gender show women as more reluctant to use these systems and the elderly as slightly more confident about them. Organisations offering similar services should consider the above results when integrating biometric verification.

They should test the acceptability among users and include mechanisms to operationalise data protection requirements into data management protocols properly (for instance, through informed consent). In this regard, they should consider the impact of introducing biometric technologies on protected groups and test them thoroughly to ensure that they do not contribute to the marginalization and exclusion of such groups.

References

Chek Ling Ngo, D. (2015). Biometric Security. Cambridge Scholars Publishing, Cambridge.

Gupta, S. and B. Crispo (2019). “A Perspective Study Towards Biometric-based Rider Authentication Schemes For Driverless Taxis,” 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 1-6, doi: 10.1109/3ICT.2019.8910310.

Riaz, S., Mushtaq, A., Pham, H., Mookim, S., & Phan, T. (2022). Analysis of Perceived Usability, Satisfaction and Adoption of Biometric Systems in the Public Transportation Sector of UAE. In Proceedings of Third International Conference on Sustainable Computing (pp. 1-12). Springer, Singapore.

Venkatesh V.; Thong J. Y. L. and Xu, X. (2012). “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, 36(1): 157-178.

Venkatesh, V.; Morris, D. (2003). “User Acceptance of Information Technology: Toward a Unified View”, MIS Quarterly, 27(3): 425-478.

Virginia Bertelli, Ethics and Technology Researcher at Eticas

• Social scientist recently graduated from MSc Social Research at Goldsmiths, University of London.
• Expertise in research methodologies, ethics, and AI.

Francesca Trevisan, Researcher and Project Manager at Eticas

• Social scientist who is completing the PhD in Social Psychologist at the University of Surrey.
• Associate Lecturer in Modelling Social Data II at Goldsmiths, University of London.
• Expertise in social justice, security and AI.

Mariano Zamorano, Senior researcher and Project manager at Eticas

• PhD in cultural policies from University of Barcelona (UB)
• Postdoctoral fellow at the UB and Associate Lecturer at the Open University of Catalonia
• Expertise in social impact of technology, responsible research and algorithmic impact assessments