Safety, privacy, or both: evaluating citizens’ perspectives around artificial intelligence use by police forces

References

• Akhgar, B., et al., 2022. Accountability principles for artificial intelligence (AP4AI) in the internet security domain. AP4AI Project. Available from: https://www.europol.europa.eu/cms/sites/default/files/documents/Accountability_Principles_for_Artificial_Intelligence_AP4AI_in_the_Internet_Security_Domain.pdf [Accessed 12 March 2022].
   Google Scholar
• Amnesty International., 2015. https://www.amnesty.org/en/latest/news/2015/03/global-opposition-to-usa-big-brother-mass-surveillance/
   Google Scholar
• Babuta, A. and Oswald, M., 2020. Data analytics and algorithms in policing in England and Wales. RUSI. Available from: https://rusi.org/explore-our-research/projects/data-analytics-and-algorithms-in-policing [Accessed 22 March 2022].
   Google Scholar
• Ball, K., 2005. Organization, surveillance, and the body: towards a politics of resistance. Organization, 12 (1), 89–108.
   Web of Science ®Google Scholar
• Bayerl, P.S., et al., 2021. Artificial intelligence in law enforcement: cross-country comparison of citizens in Greece, Italy and Spain. S. N.-R. Intelligence: Springer Nature, 11769.
   Google Scholar
• Benjamin, G., 2020. Facial recognition is spreading faster than you realise. The Conversation. Available from: https://theconversation.com/facial-recognition-is-spreading-faster-than-you-realise-132047 [Accessed 15 February 2022].
   Google Scholar
• Bentham, J., 1791. Panopticon, or, the inspection-house. Dublin, Ireland Printed: London Reprinted: T. Payne.
   Google Scholar
• Bradford, B., et al., 2020. Live facial recognition: trust and legitimacy as predictors of public support for police use of new technology. SocArXiv. doi:10.31235/osf.io/n3pwa.
   Google Scholar
• Braun, V. and Clarke, V., 2006. Using thematic analysis in psychology. Qualitative research in psychology, 3 (2), 77–101.
   Google Scholar
• Brown, S.R., 1980. Political subjectivity: applications of Q methodology in political science. New Haven, CT: Yale University Press.
   Google Scholar
• Brown, S.R., 1993. A primer on Q methodology. Operant subjectivity, 16 (3/4), 91–138.
   Google Scholar
• Burt, C. and Stephenson, W., 1939. Alternative views on correlations between persons. Psychometrika, 4 (4), 269–281.
   Google Scholar
• Bushway, S., 2020. Nothing is more opaque than absolute transparency: the use of prior history to guide sentencing. Harvard data science review, 2 (1). doi:10.1162/99608f92.468468af.
   Google Scholar
• Carrasco, M., Mills, S., Whybrew, A. and Jura, A. 2019. The Citizen’s Perspective on the Use of AI in Government. BCG Digital Government Benchmarking. Retrieved from https://www.bcg.com/publications/2019/citizen-perspective-use-artificial-intelligence-government-digital-benchmarking.aspx
   Google Scholar
• Churruca, K., Ludlow, K. and Wu, W, 2021. A scoping review of Q-methodology in healthcare research. BMC Med Res Methodol, 21, 125.
   PubMed Web of Science ®Google Scholar
• Committee of the Regions for Artificial Intelligence in Europe Brussels, 2018. AI Communication from the Commission to the EU Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the regions on Artificial Intelligence for Europe. Brussels: Europe. Available from: AI Communication (europa.eu).
   Google Scholar
• Curt, B., 1994. Textuality and tectonics: troubling social and psychological science. Buckingham: Open University Press.
   Google Scholar
• David, S.W. and Williams, L.M., 2013. Policing cybercrime: networked and social media technologies and the challenges for policing. Policing and society, 23 (4), 409–412. doi:10.1080/10439463.2013.780222.
   Web of Science ®Google Scholar
• Davis, D. and Silver, B., 2004. Civil liberties vs. security: public opinion in the context of the terrorist attacks on America. American journal of political science, 48 (1), 28–46. doi:10.1111/j.0092-5853.2004.00054.
   Web of Science ®Google Scholar
• Dencik, L., Hintz, A., and Cable, J., 2016. Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big data & society, 3 (2). doi:10.1177/2053951716679678.
   Web of Science ®Google Scholar
• Di Vaio, A., Hassan, R., and Alavoine, C, 2022. Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technological Forecasting and Social Change (174), 121201. doi:10.1016/j.techfore.2021.121201.
   Google Scholar
• EDEN Europol Conference Report, 2018. Freedom AND security killing the zero-sum process #kill0sum. Available from: https://www.europol.europa.eu/sites/default/files/documents/report_of_eden_conference_freedom_and_security_2018.pdf.
   Google Scholar
• Ellingsen, I., Størksen, I., and Stephens, P., 2010. Q methodology in social work research. International journal of social research methodology, 13 (5), 395–409.
   Web of Science ®Google Scholar
• European Digital Rights and Edinburgh International Justice Initiative, 2021. The rise and rise of biometric mass surveillance in the EU report. Brussels: EDRi and EIJI. Available from: https://edri.org/wpcontent/uploads/2021/11/EDRI_RISE_REPORT.pdf [Accessed 3 April 2022].
   Google Scholar
• Feldstein, S., 2019. The global expansion of AI surveillance. Carnegie Endowment for International Peace. Available from: https://www.carnegieendowment.org/2019/09/17/global-expansion-of-ai-surveillance-pub-79847/ [Accessed 17 April 2022].
   Google Scholar
• Foucault, M., 1977. Discipline and punish: the birth of the prison. New York: A Division of Random House, INC. Vintage Books.
   Google Scholar
• Foucault, M., 1991. Discipline and punish: the birth of the prison. London: Penguin Books.
   Google Scholar
• Fussey, P., Davies, B., and Innes, M., 2021. ‘Assisted’ facial recognition and the reinvention of suspicion and discretion in digital policing. The British journal of criminology, 61 (2), 325–344. doi:10.1093/bjc/azaa068.
   Web of Science ®Google Scholar
• Fussey, P. and Sandhu, A., 2022. Surveillance arbitration in the era of digital policing. Theoretical criminology, 26 (1), 3–22. doi:10.1177/1362480620967020.
   Web of Science ®Google Scholar
• Getting the future right: artificial intelligence and fundamental rights. European Union Agency for Fundamental Rights, 2021. Available from: https://fra.europa.eu/sites/default/files/fra_uploads/fra-2021-artificial-intelligence-summary_en.pdf.
   Google Scholar
• Gurinskaya, A., 2020. Predicting citizens’ support for surveillance cameras. Does police legitimacy matter? International journal of comparative and applied criminal justice, 44 (1–2), 63–83. doi:10.1080/01924036.2020.1744027.
   Web of Science ®Google Scholar
• Heaven, W., 2020. Predictive policing algorithms are racist. They need to be dismantled. MIT Technology Review. Available from: https://www.technologyreview.com/2020/07/17/1005396/predictive-policing-algorithms-racist-dismantled-machine-learning-bias-criminal-justice [accessed April 4, 2022].
   Google Scholar
• Jahankhani, H., et al., 2020. Policing in the era of AI and smart societies. Champ: Springer.
   Google Scholar
• Kaufmann, M., Egbert, S., and Leese, M., 2019. Predictive policing and the politics of patterns. The British journal of criminology, 59 (3), 674–692. doi:10.1093/bjc/azy060.
   Web of Science ®Google Scholar
• Lee, N. and Chin, C., 2022. Police surveillance and facial recognition: why data privacy is an imperative for communities of color. Brookings. Available from: https://www.brookings.edu/research/police-surveillance-and-facial-recognition-why-data-privacy-is-an-imperative-for-communities-of-color/.
   Google Scholar
• Leman-Langlois, S., 2002. The myopic panopticon: the social consequences of policing through the lens. Policing and society, 13 (1), 43–58. doi:10.1080/1043946022000005617.
   Google Scholar
• Lockey, S., Gillespie, N., and Curtis, C., 2020. Trust in artificial intelligence: Australian insights. The University of Queensland and KPMG Australia. Available from: https://www.assets.kpmg/content/dam/kpmg/au/pdf/2020/public-trust-in-ai.pdf [Accessed 10 March 2022].
   Google Scholar
• Lyon, D., 2002. Everyday surveillance: personal data and social classifications. Information, communication & society, 5 (2), 242–257. doi:10.1080/13691180210130806.
   Google Scholar
• Mann, S. and Ferenbok, J., 2013. New media and the power politics of sousveillance in a surveillance-dominated world. Surveillance & society, 11 (1/2), 18–34.
   Google Scholar
• Marx, G., 1988. Undercover: police surveillance in America. Berkley: University of California Press.
   Google Scholar
• Marx, G., 2009. A tack in the shoe and taking off the shoe neutralization and counter-neutralization dynamics. Surveillance & society, 6 (3), 294–306.
   Google Scholar
• McGuire, M., 2020. The laughing policebot: automation and the end of policing. Policing and society, 31 (1), 20–36.
   Web of Science ®Google Scholar
• McKeown, B. and Thomas, D., 1988. Q methodology: quantitative applications in the social sciences. London: Sage.
   Google Scholar
• Metropolitan Police and NPL, 2020. Metropolitan police service live facial recognition trials. Available from: https://www.met.police.uk/SysSiteAssets/media/downloads/central/services/accessing-information/facial-recognition/met-evaluation-report.pdf.
   Google Scholar
• Morgenstern, J., et al., 2021. ‘AI’s gonna have an impact on everything in society, so it has to have an impact on public health’: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC public health, 21 (1), 40. doi:10.1186/s12889-020-10030-x.
   PubMed Web of Science ®Google Scholar
• Moses, B.L. and Chan, J., 2016. Algorithmic prediction in policing: assumptions, evaluation, and accountability. Policing and society, 28 (7), 806–822. doi:10.1080/10439463.2016.1253695.
   Web of Science ®Google Scholar
• Nalla, K.M., Gorazd, M., and Maja, M., 2018. Assessing police–community relationships: is there a gap in perceptions between police officers and residents? Policing and society, 28 (3), 271–290. doi:10.1080/10439463.2016.1147564.
   Web of Science ®Google Scholar
• Neudert, L.M., Knuutila, A., and Howard, P., 2020. Global attitudes towards AI, machine learning & automated decision making. Implications for involving artificial intelligence in public service and good governance. Oxford: University of Oxford: Oxford Commission on AI & Good Governance.
   Google Scholar
• Orwell, G., 2000. 1984 nineteen eighty-four. Berkley: Penguin Random House.
   Google Scholar
• Oswald, M., et al., 2018. Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘experimental’ proportionality. Information & communications technology law, 27 (2), 223–250. doi:10.1080/13600834.2018.1458455.
   Web of Science ®Google Scholar
• Patton, M.Q., 1990. Qualitative evaluation and research methods. 2nd ed. New Bury Park, CA: Sage.
   Google Scholar
• Pavone, V. and Esposti, S., 2012. Public assessment of new surveillance-oriented security technologies: beyond the trade-off between privacy and security. Public understanding of science, 21 (5), 556–572. doi:10.1177/0963662510376886.
   PubMed Web of Science ®Google Scholar
• Quattrocolo, S., 2020. Equality of arms and automatedly generated evidence. In: Lorena Bachmaier Winter, ed. The artificial intelligence, computational modelling and criminal proceedings. Italy: Springer International Publishing, 73–98.
   Google Scholar
• Regan, P., 1995. Legislating privacy: technology, social values, and public policy. Chapel Hill: University of North Carolina Press.
   Google Scholar
• Rovatsos, M., Mittelstadt, B., and Koene, A., 2019. Bias in algorithmic decision making. The University of Edinburgh: Centre for Data Ethics and Innovation. UK Government. https://www.gov.uk/government/publications/landscape-summaries-commissioned-by-the-centre-for-data-ethics-and-innovation.
   Google Scholar
• Schmolck, P. 2014. PQMethod Software Package [Computer Software]. Retrieved from http://schmolck.org/qmethod/downpqwin.htm
   Google Scholar
• Schneier, B., 2012. How changing technology affects security. IEEE security & privacy magazine, 10 (2), 104–104. doi:10.1109/msp.2012.39.
   Web of Science ®Google Scholar
• Smith, G., Moses, B.L., and Chan, J., 2017. The challenges of doing criminology in the big data era: towards a digital and data-driven approach. The British journal of criminology, 57 (2), 259–274. doi:10.1093/bjc/azw096.
   Web of Science ®Google Scholar
• Solove, D., 2011. Nothing to hide: the false tradeoff between privacy and security chapter in NOTHING TO HIDE: THE FALSE TRADEOFF BETWEEN PRIVACY AND SECURITY. New Haven and London: Yale University Press.
   Google Scholar
• Stenner, P., Dancey, C., and Watts, S., 2000. The understanding of their illness amongst people with irritable bowel syndrome: a Q methodological study. Social science and medicine, 51 (3), 439–452.
   PubMed Web of Science ®Google Scholar
• Tounsi, W. and Rais, H., 2018. A survey on technical threat intelligence in the age of sophisticated cyber attacks. Computers & security, 72, 212–233. doi:10.1016/j.cose.2017.09.001.
   Web of Science ®Google Scholar
• Trottier, D., 2017. ‘Fear of contact’: police surveillance through social networks. European journal of cultural and political sociology, 4 (4), 457–477. doi:10.1080/23254823.2017.1333442.
   Google Scholar
• Turner, E., Medina, J., and Brown, G., 2019. Dashing hopes? The predictive accuracy of domestic abuse risk assessment by police. The British journal of criminology, 59 (5), 1013–1034. doi:10.1093/bjc/azy074.
   Web of Science ®Google Scholar
• United Nations Interregional Crime and Justice Research Institute (UNICRI) and International Criminal Organization (Interpol), 2019. Artificial intelligence and robotics for law enforcement. UNICRI and Interpol. Available from: http://www.unicri.it/artificial-intelligence-and-robotics-law-enforcement [Accessed 20 April 2022].
   Google Scholar
• Urquhart, L. and Miranda, D., 2022. Policing faces: the present and future of intelligent facial surveillance. Information & communications technology law, 31 (2), 194–219. doi:10.1080/13600834.2021.1994220.
   Web of Science ®Google Scholar
• Watts, S. and Stenner, P., 2012. Doing Q methodological research: theory, method, and interpretation. London: Sage.
   Google Scholar
• Westacott, E., 2010. Does surveillance make us morally better. Philosophy now, 79, 6–9.
   Google Scholar
• Završnik, A., 2020. Criminal justice, artificial intelligence systems, and human rights. ERA forum, 20, 567–583. doi:10.1007/s12027-020-00602-0.
   Google Scholar