Algorithmic policing accountability: eight sociotechnical challenges

References

• Afzal, M., and Panagiotopoulos, P., 2020. Smart policing: a critical review of the literature. In: Viale Pereira G., M. Janssen, H. Lee, I. Lindgren, P. Rodríguez Bolívar, H.J. Scholl, and A. Zuiderwijk, eds. Electronic government. EGOV 2020. lecture notes in computer science, vol 12219. Cham: Springer, 59–70.
   Google Scholar
• Almeida, D., Shmarko, K., and Lomas, E., 2021. The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks. Ai and ethics, 2 (3), 377–387.
   PubMedGoogle Scholar
• Ananny, M., and Crawford, K., 2018. Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability. New media & society, 20 (3), 973–989.
   Web of Science ®Google Scholar
• Bannister, F., and Connolly, R., 2020. Administration by algorithm: a risk management framework. Information polity, 25 (4), 471–490.
   Web of Science ®Google Scholar
• Beer, D., 2017. The social power of algorithms. Information, communication & society, 20 (1), 1–13.
   Web of Science ®Google Scholar
• Bennet Moses, L., and Chan, J., 2018. Algorithmic prediction in policing: assumptions, evaluation, and accountability. Policing and society, 28 (7), 806–822.
   Web of Science ®Google Scholar
• Binns, R., 2018. Algorithmic accountability and public reason. Philosophy & Technology, 31 (4), 543–556.
   PubMedGoogle Scholar
• Borges, A.F.S., et al., 2021. The strategic use of artificial intelligence in the digital era: systematic literature review and future research directions. International journal of information management, 57, 1–16.
   Web of Science ®Google Scholar
• Bovens, M., 2007. Analysing and assessing accountability: a conceptual framework. European Law journal, 13 (4), 447–468.
   Web of Science ®Google Scholar
• Bovens, M., and Zouridis, S., 2002. From street-level to system-level bureaucracies: how information and communication technology is transforming administrative discretion and constitutional control. Public administration review, 62 (2), 174–184.
   Web of Science ®Google Scholar
• Boyd, D., and Crawford, K., 2012. Critical questions for big data. Information, communication & society, 15 (5), 662–679.
   Web of Science ®Google Scholar
• Brannon, M.M., 2017. Datafied and divided: techno–dimensions of inequality in American cities. City & community, 16 (1), 20–24.
   Web of Science ®Google Scholar
• Brayne, S., 2017. Big data surveillance: the case of policing. American sociological review, 82 (5), 977–1008.
   PubMed Web of Science ®Google Scholar
• Brayne, S., 2020. Predict and surveil: data, discretion, and the future of policing, predict and surveil: data, discretion, and the future of policing. New York: Oxford University Press.
   Google Scholar
• Busch, P.A., and Henriksen, H.Z., 2018. Digital discretion: a systematic literature review of ICT and street-level discretion. Information polity, 23 (1), 3–28.
   Web of Science ®Google Scholar
• Busuioc, M., 2021. Accountable artificial intelligence: holding algorithms to account. Public administration review, 81 (5), 825–836.
   PubMed Web of Science ®Google Scholar
• Cabiddu, F., et al., 2022. Why do users trust algorithms? A review and conceptualization of initial trust and trust over time. European management journal, 40 (5), 685–706.
   Google Scholar
• Canter, D., et al., 2013. The efficacy of ideographic models for geographical offender profiling. Journal of quantitative criminology, 29 (3), 423–446.
   Web of Science ®Google Scholar
• Carter, J.G., et al., 2021. The Indianapolis harmspot policing experiment. Journal of criminal justice, 74, 101814–16.
   Web of Science ®Google Scholar
• Chan, J., and Moses, L.B., 2017. Making sense of big data for security. British journal of criminology, 57 (2), 299–319.
   Web of Science ®Google Scholar
• Clarke, R., 2016. Big data, big risks. Information systems journal, 26 (1), 77–90.
   Web of Science ®Google Scholar
• Coglianese, C., 2021. Algorithmic regulation machine learning as a governance tool. In: M. Schuilenburg, and R. Peeters, eds. The algorithmic society: technology, power, and knowledge. London: Routledge, 35–52.
   Google Scholar
• Das, A., and Schuilenburg, M., 2020. Garbage in, garbage out. Beleid en Maatschappij, 47 (3), 254–268.
   Google Scholar
• Dechesne, F., et al. 2019. Ai & ethics at the police: towards responsible use of artificial intelligence in the Dutch police, Leiden University Center for Law and Digital Technologies and TU Delft Institute of Design For Values. Available from: https://scholarlypublications.universiteitleiden.nl/access/item:2983928/view.
   Google Scholar
• de Visser, E.J., Pak, R., and Shaw, T.H., 2018. From “automation” to “autonomy”: the importance of trust repair in human–machine interaction. Ergonomics, 61 (10), 1409–1427.
   PubMed Web of Science ®Google Scholar
• Edwards, A., 2017. Big data, predictive machines and security: The minority report 1. In: M.R. McGuire, and T. Holt, eds. The Routledge handbook of technology, crime and justice. London: Routledge, 451–461.
   Google Scholar
• Egbert, S., and Krasmann, S., 2020. Predictive policing: not yet, but soon preemptive? Policing and society, 30 (8), 905–919.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Fussey, P., and Murray, D. 2019. Independent report on the London metropolitan police services trial of live facial recognition technology. The human rights, big data and technology project. Available from: https://48ba3m4eh2bf2sksp43rq8kk-wpengine.netdna-ssl.com/wp-content/uploads/2019/07/London-Met-Police-Trial-of-Facial-Recognition-Tech-Report.pdf.
   Google Scholar
• Fussey, P., and Sandhu, A., 2020. Surveillance arbitration in the era of digital policing. Theoretical criminology, 26 (1), 3–22.
   Web of Science ®Google Scholar
• Harris, H., 2022. D. Goldbarsht, and L. de Koker, eds. Artificial intelligence and policing of financial crime: a legal analysis of the state of the field BT - financial technology and the law : combating financial crime. Cham: Springer International Publishing, 281–299.
   Google Scholar
• Hayward, K.J., and Maas, M.M., 2021. Artificial intelligence and crime: a primer for criminologists. Crime, media, culture: An international journal, 17 (2), 209–233.
   Web of Science ®Google Scholar
• Hung, T.W., and Yen, C.P., 2021. On the person-based predictive policing of AI. Ethics and information technology, 23 (3), 165–176.
   Web of Science ®Google Scholar
• Hunt, P., Saunders, J., and Hollywood, J.S. 2014. Evaluation of the Shreveport predictive policing experiment. Available from: http://www.rand.org/content/dam/rand/pubs/research_reports/RR500/RR531/RAND_RR531.pdf.
   Google Scholar
• Hupe, P., and Hill, M., 2007. Street-level bureaucracy and public accountability. Public administration, 85 (2), 279–299.
   Web of Science ®Google Scholar
• Jarrahi, M.H., et al., 2021. Algorithmic management in a work context. Big data & society, 8 (2), 205395172110203–14.
   Web of Science ®Google Scholar
• Joh, E.E., 2019. Policing the smart city. International journal of Law in context, 15 (2), 177–182.
   Web of Science ®Google Scholar
• Joyce, K., et al., 2021. Toward a sociology of artificial intelligence: a call for research on inequalities and structural change. Socius sociological research for a dynamic world, 7 (4), 1–11.
   Google Scholar
• Karppi, T., 2018. “The computer said so”: on the ethics, effectiveness, and cultural techniques of predictive policing. Social media and society, 4 (2), 1–9.
   Web of Science ®Google Scholar
• Kaufmann, M., 2019. Who connects the dots?: Agents and agency in predictive policing. In: M. Hoijtink, and M. Leese, eds. Technology and agency in international relations. New York: Routledge, 141–163.
   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.
   Web of Science ®Google Scholar
• Kaur, P., et al., 2020. Facial-recognition algorithms: a literature review. Medicine, Science and the Law, 60 (2), 131–139.
   PubMed Web of Science ®Google Scholar
• Kemper, J., and Kolkman, D., 2019. Transparent to whom? No algorithmic accountability without a critical audience. Information, communication & society, 22 (14), 2081–2096.
   Web of Science ®Google Scholar
• Kitchin, R., 2013. Big data and human geography: opportunities, challenges and risks. Dialogues in human geography, 3 (3), 262–267.
   Google Scholar
• Kitchin, R., 2017. Thinking critically about and researching algorithms. Information, communication & society, 20 (1), 14–29.
   Web of Science ®Google Scholar
• Kitchin, R., and McArdle, G., 2016. What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big data & society, 3 (1), 1–10.
   Web of Science ®Google Scholar
• König, P.D., and Wenzelburger, G., 2021. The legitimacy gap of algorithmic decision-making in the public sector: why it arises and how to address it. Technology in society, 67, 101688–10.
   Web of Science ®Google Scholar
• Kotsoglou, K.N., and Oswald, M., 2020. The long arm of the algorithm? Automated facial recognition as evidence and trigger for police intervention. Forensic science international: synergy, 2, 86–89.
   PubMedGoogle Scholar
• Lacinák, M., and Ristvej, J., 2017. Smart city, safety and security. Procedia engineering, 192, 522–527.
   Google Scholar
• Laufs, J., Borrion, H., and Bradford, B., 2020. Security and the smart city: a systematic review. Sustainable cities and society, 55, 102023–18.
   Web of Science ®Google Scholar
• Lecun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning. Nature, 521 (7553), 436–444.
   PubMed Web of Science ®Google Scholar
• Leese, M., 2021. Security as socio-technical practice: predictive policing and, non-)automation. Swiss political science review, 27 (1), 150–157.
   Web of Science ®Google Scholar
• Levine, E.S., et al., 2017. The New York City police departments domain awareness system. Interfaces, 47 (1), 70–84.
   Web of Science ®Google Scholar
• Lipsky, M., 1980. Street-level bureaucracy: dilemmas of the individual in public service. New York: Russell Sage Foundation.
   Google Scholar
• Loader, I., and Walker, N., 2006. Necessary virtues: the legitimate place of the state in the production of security. In: B. Dupont, and J. Wood, eds. Democracy, society and the governance of security. Cambridge: Cambridge University Press, 165–195.
   Google Scholar
• Lubna, Mufti, N., and Shah, S.A.A., 2021. Automatic number plate recognition: a detailed survey of relevant algorithms. Sensors, 21 (9), 3028–3035.
   PubMed Web of Science ®Google Scholar
• Lum, K., and Isaac, W., 2016. To predict and serve? Significance, 13 (5), 14–19.
   Google Scholar
• Lyons, J.B., et al., 2019. Trust and human-machine teaming: a qualitative study. In: W. Lawless, R. Mittu, D Sofge, I.S. Moskowitz, and S. Russel, eds. Artificial intelligence for the internet of everything. London: Academic Press, 101–116.
   Google Scholar
• Makarius, E.E., et al., 2020. Rising with the machines: a sociotechnical framework for bringing artificial intelligence into the organization. Journal of business research, 120, 262–273.
   Web of Science ®Google Scholar
• Manning, P., 2019. The rise of big data policing: surveillance, race, and the future of law enforcement. Contemporary sociology: a journal of reviews, 48 (2), 170–171.
   Web of Science ®Google Scholar
• Marciniak, D., 2023. Algorithmic policing : an exploratory study of the algorithmically mediated construction of individual risk in a UK police force mediated construction of individual risk in a UK police force. Policing and society, 33 (4), 449–463.
   Web of Science ®Google Scholar
• McGuire, M.R., 2021. The laughing policebot: automation and the end of policing. Policing and society, 31 (1), 20–36.
   Web of Science ®Google Scholar
• Meijer, A., and Grimmelikhuijsen, S., 2021. Responsible and accountable algorithmization: how to generate citizen trust in governmental usage of algorithms. In: M. Schuilenburg, and R. Peeters, eds. The algorithmic society: power, knowledge and technology in the Age of algorithms. London: Routledge, 53–66.
   Google Scholar
• Meijer, A., Lorenz, L., and Wessels, M., 2021. Algorithmization of bureaucratic organizations: using a practice lens to study how context shapes predictive policing systems. Public administration review, 81 (5), 837–846.
   Web of Science ®Google Scholar
• Meijer, A., and Wessels, M., 2019. Predictive policing: review of benefits and drawbacks. International journal of public administration, 42 (12), 1031–1039.
   Web of Science ®Google Scholar
• Meterko, V., and Cooper, G., 2022. Cognitive biases in criminal case evaluation. A review of the research. Journal of Police and Criminal Psychology, 37 (1), 101–122.
   Web of Science ®Google Scholar
• Molander, A., Grimen, H., and Eriksen, E.O., 2012. Professional discretion and accountability in the welfare state. Journal of applied philosophy, 29 (3), 214–230.
   Web of Science ®Google Scholar
• Noori, N., et al., 2021. Input-output modeling for smart city development. Journal of urban technology, 28 (1–2), 71–92.
   Web of Science ®Google Scholar
• Oosterloo, S., et al., 2018. The politics and biases of the “crime anticipation system” of the Dutch police. Proceedings of the international workshop on bias in information, algorithms, and systems, 2103, 30–41.
   Google Scholar
• Overman, S., Schillemans, T., and Grimmelikhuijsen, S., 2021. A validated measurement for felt relational accountability in the public sector: gauging the account holder’s legitimacy and expertise. Public management review, 23 (12), 1748–1767.
   Web of Science ®Google Scholar
• Park, S., and Gil-Garcia, J.R., 2022. Open data innovation: visualizations and process redesign as a way to bridge the transparency-accountability gap. Government information quarterly, 39, 101456–10.
   Web of Science ®Google Scholar
• Peeters, R., 2020. The agency of algorithms: understanding human-algorithm interaction in administrative decision-making. Information polity, 25 (4), 507–522.
   Web of Science ®Google Scholar
• Peeters, R., and Widlak, A., 2018. The digital cage: administrative exclusion through information architecture – the case of the Dutch civil registry’s master data management system. Government information quarterly, 35 (2), 175–183.
   Web of Science ®Google Scholar
• Perry, W.L., et al., 2013. Predictive policing: the role of crime forecasting in law enforcement operations. Santa Monica, CA: RAND Corporation.
   Google Scholar
• Phillips-Wren, G., et al., 2009. An integrative evaluation framework for intelligent decision support systems. European journal of operational research, 195 (3), 642–652.
   Web of Science ®Google Scholar
• Politie. 2021. ‘20210406 - 8313 - Besluit Wob-verzoek Criminaliteits Anticipatie Systeem_Geredigeerd’. Available from: https://www.politie.nl/binaries/content/assets/politie/wob/00-landelijk/criminaliteits-anticipatie-systeem/20210406—8313—besluit-wob-verzoek-criminaliteits-anticipatie-systeem_geredigeerd.pdf.
   Google Scholar
• Richardson, R., Schultz, J.M., and Crawford, K., 2019. Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. NYUL Rev. Online, 94, 1–41.
   Google Scholar
• Ridgeway, G., 2018. Policing in the era of big data. Annual review of criminology, 1, 401–419.
   Web of Science ®Google Scholar
• Sanders, C.B., and Langan, D., 2018. New public management and the extension of police control: community safety and security networks in Canada. Policing and society, 29 (5), 566–578.
   Web of Science ®Google Scholar
• Sandhu, A., and Fussey, P., 2021. The “uberization of policing”? How police negotiate and operationalise predictive policing technology. Policing and society, 31 (1), 66–81.
   Web of Science ®Google Scholar
• Saunders, J., Hunt, P., and Hollywood, J.S., 2016. Predictions put into practice: a quasi-experimental evaluation of Chicago’s predictive policing pilot. Journal of experimental criminology, 12 (3), 347–371.
   Web of Science ®Google Scholar
• Schillemans, T., and Busuioc, M., 2015. Predicting public sector accountability: from agency drift to forum drift. Journal of public administration research and theory, 25 (1), 191–215.
   Web of Science ®Google Scholar
• Schuilenburg, M., and Pali, B., 2021. Smart city imaginaries: looking beyond the techno-utopian vision. In: M. Schuilenburg, and R. Peeters, eds. The algorithmic society: power, knowledge and technology in the Age of algorithms. New York: Routledge, 137–153.
   Google Scholar
• Schuilenburg, M., and Peeters, R., 2021. Understanding the algorithmic society: Concluding thoughts. In: M. Schuilenburg, and R. Peeters, eds. The algorithmic society: technology, power, and knowledge. London: Routledge, 193–200.
   Google Scholar
• Sheehey, B., 2019. Algorithmic paranoia: the temporal governmentality of predictive policing. Ethics and information technology, 21 (1), 49–58.
   Web of Science ®Google Scholar
• Singh, U., et al., 2021. Crowd monitoring: state-of-the-art and future directions. Iete technical review, institution of electronics and telecommunication engineers, India, 38 (6), 578–594.
   Google Scholar
• Snaphaan, T., and Hardyns, W., 2021. Environmental criminology in the big data era. European journal of criminology, 18 (5), 713–734.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Van Brakel, R., 2016. Pre-emptive big data surveillance and its (Dis)empowering consequences: the case of predictive policing. In: B. Van der Sloot, D. Broeders, and E. Schrijvers, eds. Exploring the boundaries of Big data. Amsterdam: Amsterdam University Press, 117–141.
   Google Scholar
• Van Brakel, R., 2020. Rethinking predictive policing. In: M. Schuilenburg, and R. Peeters, eds. The algorithmic society: technology, power, and knowledge. London: Routledge, 104–118.
   Google Scholar
• Van Brakel, R., 2021. How to watch the watchers? Democratic oversight of algorithmic police surveillance in Belgium. Surveillance & Society, 19 (2), 228–240.
   Web of Science ®Google Scholar
• Van Brakel, R., and De Hert, P., 2011. Policing, surveillance and law in a pre-crime society: understanding the consequences of technology based strategies. Cahiers politiestudies, 3 (20), 163–192.
   Google Scholar
• van Eijk, G., 2020. Algorithmic reasoning: the production of subjectivity through data. In: M. Schuilenburg, and R. Peeters, eds. The algorithmic society: technology, power, and knowledge. London: Routledge, 119–134.
   Google Scholar
• van Ooijen, C., 2011. Legitimacy issues regarding citizen surveillance: the case of ANPR technology in Dutch policing BT. In: S. van der Hof, and M.M. Groothuis, eds. Innovating government: normative, policy and technological dimensions of modern government. The Hague, The Netherlands: T.M.C. Asser Press, 197–216.
   Google Scholar
• Vepřek, L.H., et al., 2020. Beyond effectiveness : legitimising predictive policing in Germany. Criminology-the online journal, 3, 423–443.
   Google Scholar
• Vestby, A., and Vestby, J., 2021. Machine learning and the police: asking the right questions. Policing, 15 (1), 44–58.
   Google Scholar
• Vogl, T.M., et al., 2020. Smart technology and the emergence of algorithmic bureaucracy: artificial intelligence in UK local authorities. Public administration review, 80 (6), 946–961.
   Web of Science ®Google Scholar
• Waardenburg, L., Huysman, M., and Sergeeva, A.V., 2022. In the land of the blind, the one-eyed man is king: knowledge brokerage in the age of learning algorithms. Organization science, 33 (1), 59–82.
   Web of Science ®Google Scholar
• Wieringa, M., 2020. What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. Fat 2020 - proceedings of the 2020 conference on fairness, accountability, and transparency, January 27-30, 2020, 1–18.
   Google Scholar
• Wieringa, M., 2023. “Hey SyRI, tell me about algorithmic accountability”: lessons from a landmark case. Data & Policy, 5, e2 1–e24.
   Google Scholar
• Win, K.N., et al., 2020. Fingerprint classification and identification algorithms for criminal investigation: a survey. Future generation computer systems, 110, 758–771.
   Web of Science ®Google Scholar
• Young, M., Katell, M., and Krafft, P.M., 2019. Municipal surveillance regulation and algorithmic accountability. Big data & society, 6 (2), 1–14.
   Web of Science ®Google Scholar
• Zuiderwijk, A., Chen, Y.C., and Salem, F., 2021. Implications of the use of artificial intelligence in public governance: a systematic literature review and a research agenda. Government information quarterly, 38 (3), 1–19.
   Web of Science ®Google Scholar