Abstract
Considering the lack of consensus on the deployment of facial recognition technologies, many organizations, including public bodies, NGOs and private companies have alerted public opinion and called for a broad debate on facial recognition. We believe that such a debate is indeed necessary. However, in order to be really productive, it is essential to ensure that arguments can be expressed and confronted in a rigorous way. The main objective of this position paper is to help set the terms of this debate on a solid basis. To this aim, we present an incremental and comparative risk-analysis methodology for facial recognition systems. The methodology introduces, for a better separation of concerns, four levels of analysis: the purpose, the means, the use of facial recognition and its implementation. We discuss each of these levels and illustrate them with examples based on recent developments. Interested readers can find more details, in particular about the use of ethical matrices to facilitate the analysis, in an extended version of this position paper published as an Inria report [7].
The authors would like to thank Clément Henin and Vincent Roca for their constructive comments on an earlier draft of this paper. This work is supported by the French National Research Agency in the framework of the “Investissements d’avenir” program (ANR-15-IDEX-02).
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Notes
- 1.
How facial recognition makes you safer, James O’Neill, New York Times, 9 June 2019.
- 2.
For example, CNIL (Commission Nationale de l’Informatique et des Libertés), the French Data Protection Authority; ICO (Information Commissioner’s Office), the UK Data Protection Authority; the AINow Institute; ACLU (American Civil Liberties Union); EFF (Electronic Frontier Foundation); Google; Microsoft, etc.
- 3.
ALICEM (acronym in French for “certified online authentication on mobile phones”) allows a user to generate a secure digital identity remotely. Identification is carried out by presenting the passport. The system extracts the information from the passport (identity and a photograph of the holder) and asks the user to take a video of his face. The information is sent to a server that compares the person’s face on the video and the passport photograph. If successful, the user is authenticated.
- 4.
In some cases, the database can be distributed on devices, such as identity cards or mobile phones, and stay under the control of the users.
- 5.
- 6.
Many cases have been revealed. See for example: https://www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html, https://www.nytimes.com/interactive/2019/10/11/technology/flickr-facial-recognition.html.
- 7.
- 8.
For example, to coordinate crews in the field or to monitor major events in real time in the case of the Nice urban supervision center.
- 9.
See for example: https://www.eff.org/wp/law-enforcement-use-face-recognition.
- 10.
Stakeholders are defined as all entities, persons or groups of persons, who may be affected by a system, directly or indirectly, in an active (sponsor, developer, operator, user, etc.) or passive (citizen, passenger, etc.) manner.
- 11.
The French national DNA database (FNAEG), already mentioned, provides a prime example: created in 1998 to centralize the fingerprints of persons convicted of extremely serious offenses (murder of a minor person preceded or associated with rape, torture or barbaric acts, etc.), it has been successively extended to include nearly three million DNA profiles in 2018.
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References
Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Axon: First report of the Axon AI & Policing Technology Ethics Board (2019). https://www.policingproject.org/axon-fr
Big Brother Watch: Face off. The lawless growth of facial recognition in UK policing (2018). https://bigbrotherwatch.org.uk/wp-content/uploads/2018/05/Face-Off-final-digital-1.pdf
Buolamwini, J., Gebru, G.: Gender shades: intersectional accuracy disparities in commercial gender classification. Mach. Learn. Res. 81, 77–91 (2018)
Butin, D., Chicote, M., Le Métayer, D.: Strong accountability: beyond vague promises. In: Gutwirth, S., Leenes, R., De Hert, P. (eds.) Reloading Data Protection, pp. 343–369. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7540-4_16
Castelluccia, C., Le Métayer, D.: Understanding algorithmic decision-making: opportunities and challenges. Study for the European Parliament (STOA) (2019). https://www.europarl.europa.eu/stoa/en/document/EPRS_STU(2019)624261
Castelluccia, C., Le Métayer, D.: Impact analysis of facial recognition - towards a rigorous methodology. Inria Note (2020). https://hal.inria.fr/hal-02480647/document
CNIL: Facial recognition; for a debate living up to the challenges (2019). https://www.cnil.fr/sites/default/files/atoms/files/facial-recognition.pdf
European Commission: On Artificial Intelligence - A European approach to excellence and trust (2020). https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020-en.pdf
European Parliament: Directive 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data (2016)
European Parliament: Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation) (2016)
Forsberg, E.-M.: The ethical matrix - a tool for ethical assessments for biotechnology. Golbal Bioeth. 17, 167–172 (2004)
Georgetown Law Center on Privacy & Technology: The perpetual line-up. Unregulated police face recognition in America (2016). https://www.perpetuallineup.org/
ICO: The use of live facial recognition technology by law enforcement in public places (2019). https://ico.org.uk/media/about-the-ico/documents/2616184/live-frt-law-enforcement-opinion-20191031.pdf
Penney, J.: Chilling effects: online surveillance and wikipedia use. Berkeley Technol. Law J. 31(1), 117–182 (2016)
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Castelluccia, C., Le Métayer, D. (2020). Position Paper: Analyzing the Impacts of Facial Recognition. In: Antunes, L., Naldi, M., Italiano, G., Rannenberg, K., Drogkaris, P. (eds) Privacy Technologies and Policy. APF 2020. Lecture Notes in Computer Science(), vol 12121. Springer, Cham. https://doi.org/10.1007/978-3-030-55196-4_3
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