THE DOMINANCE OF ALGORITHMS IN PUBLIC ADMINISTRATION: ALGOCRACY AND ITS THREATS

Abstract

Modern states fulfil their functions through the bureaucratic apparatus. Today, however, the dizzying pace of technological development is transforming bureaucracies as it transforms everything. Machine learning and artificial intelligence applications, which have developed in parallel with technological developments, are increasingly dominated by algorithms in public administration. For this reason, it is claimed that bureaucracies have turned into algocracies and will continue to do so. Algocracy, a newly introduced concept, is inspired by bureaucracy and is defined as the use of power through algorithms, similar to the use of power through bureaus. However, it is debatable whether algocracy, which has been introduced as a new concept, is a completely different concept from bureaucracy. For this reason, this study first clarifies the concept of algocracy. Although there are many studies on the opportunities offered by algocracy in the literature, there is a limited number of studies on the problems caused by algocracy that become threats to citizens. For this reason, the main purpose of this study is to address the threats posed by algocracy. In this framework, the study addresses the threats posed by algocracy, such as transparency problems, discrimination (algorithms lacking objectivity), privacy violations, further centralization of governance, excessive trust in algorithms, legitimacy problems, and morality problems. The paper concludes with some recommendations, recognizing that dealing with these threats will not be easy but that solutions are possible.

Keywords

• Bureaucracy
• Algorithm
• Algocracy
• Threats

KAMU YÖNETİMİNDE ALGORİTMALARIN EGEMENLİĞİ: ALGOKRASİ VE TEHDİTLERİ

Abstract

Modern devletler işlevlerini bürokrasi aygıtı aracılığıyla yerine getirmektedir. Ancak günümüzde teknolojinin baş döndürücü bir hızla gelişmesi her şeyi dönüştürdüğü gibi bürokrasileri de dönüştürmektedir. Teknolojik gelişmelere koşut olarak gelişen makine öğrenmesi ve yapay zekâ uygulamaları kamu yönetiminde de giderek daha fazla algoritmaların hâkim olmasına neden olmaktadır. Bu nedenle bürokrasilerin algokrasiye dönüştüğü ve dönüşmeye devam edeceği iddia edilmektedir. Yeni ortaya atılan bir kavram olan algokrasi, bürokrasiden esinlenerek gücün bürolar aracılığıyla kullanmasına benzer şekilde gücün algoritmalar aracılığıyla kullanılması olarak ifade edilmektedir. Ancak yeni bir kavram olarak ortaya atılan algokrasinin bürokrasiden tamamen farklı bir kavram olup olmadığı konusu tartışmalıdır. Bu nedenle çalışmada öncelikle algokrasi kavramına açıklık getirilmektedir. Alan yazında algokrasinin sunduğu fırsatlarla ilgili çok fazla çalışma bulunmasına rağmen algokrasinin yol açtığı ve yurttaşlar için tehdit haline gelen sorunların ele alındığı çalışmalar oldukça sınırlı sayıdadır. Bu nedenle çalışmanın temel amacı algokrasinin yol açtığı tehditleri ele almak olarak belirlenmiştir. Bu çerçevede çalışmada şeffaflık sorunları başta olmak üzere ayrımcılık (tarafsızlıktan yoksun algoritmalar), kişisel mahremiyet ihlalleri, yönetimi daha fazla merkezileştirme, algoritmalara gereğinden fazla güvenme, meşruiyet ve ahlakilik sorunları gibi algokrasinin yol açtığı tehditler ele alınmaktadır. Bu tehditlerle başa çıkabilmenin hiç de kolay olmayacağı bilinmesiyle birlikte yine de çözümün mümkün olduğunu belirten çalışma birtakım önerilerde bulunarak son bulmaktadır.

Keywords

• Bürokrasi
• Algoritma
• Algokrasi
• Tehditler

References

1. Ali, M. A.,Mann, S. (2013). The inevitability of the transition from a surveillance-society to a veillance-society: Moral and economic grounding for sousveillance IEEE International Symposium on Technology and Society (ISTAS): Social Implications of Wearable Computing and Augmediated Reality in Everyday Life, http://wearcam.org/veillance/IEEE_ISTAS13_Veillance2_Ali_Mann.pdf
2. Alnemr, N. (2023). Democratic self-government and the algocratic shortcut: the democratic harms in algorithmic governance of society. Contemporary Political Theory. https://doi.org/10.1057/s41296-023-00656-y
3. Aneesh, A. (2002). Technologically coded authority: The post-industrial decline in bureaucratic hierarchies. 7th International Summer Academy on Technology Studies, Deutschlandsberg, Austria, 27-51.
4. Aneesh, A. (2009). Global Labor: Algocratic Modes of Organization*. Sociological Theory, 27(4), 347-370. https://doi.org/https://doi.org/10.1111/j.1467-9558.2009.01352.x
5. Beckman, L., Hultin Rosenberg, J.,Jebari, K. (2022). Artificial intelligence and democratic legitimacy. The problem of publicity in public authority. AI & SOCIETY. https://doi.org/10.1007/s00146-022-01493-0
6. Bertino, E.,Ferrari, E. (2018). Big Data Security and Privacy. In S. Flesca, S. Greco, E. MasciariveD. Saccà (Ed.), A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years (pp. 425-439). Springer International Publishing. https://doi.org/10.1007/978-3-319-61893-7_25
7. Bowker, G. C.,Star, S. L. (2000). Sorting things out: Classification and its consequences. MIT press.
8. Bullock, J., Young, M. M.,Wang, Y.-F. (2020). Artificial intelligence, bureaucratic form, and discretion in public service. Information Polity, 25, 491-506. https://doi.org/10.3233/IP-200223

9. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1-12. https://doi.org/10.1177/2053951715622512
10. Chomanski, B. (2022). Legitimacy and automated decisions: the moral limits of algocracy. Ethics and Information Technology, 24(3), 34. https://doi.org/10.1007/s10676-022-09647-w
11. Conway, M. E. (1968). How do committees invent. Datamation, 14(4), 28-31.
12. Criado, J. I., Valero, J.,Villodre, J. (2020). Algorithmic transparency and bureaucratic discretion: The case of SALER early warning system. Information Polity, 25, 449-470. https://doi.org/10.3233/IP-200260
13. Danaher, J. (2016). The Threat of Algocracy: Reality, Resistance and Accommodation. Philosophy & Technology, 29(3), 245-268. https://doi.org/10.1007/s13347-015-0211-1
14. Doshi-Velez, F.,Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
15. Etzioni, A.,Etzioni, O. (2017). Incorporating Ethics into Artificial Intelligence. The Journal of Ethics, 21(4), 403-418. https://doi.org/10.1007/s10892-017-9252-2
16. Felzmann, H., Fosch-Villaronga, E., Lutz, C.,Tamò-Larrieux, A. (2020). Towards Transparency by Design for Artificial Intelligence. Science and Engineering Ethics, 26(6), 3333-3361. https://doi.org/10.1007/s11948-020-00276-4
17. Floridi, L. (2012). Big Data and Their Epistemological Challenge. Philosophy & Technology, 25(4), 435-437. https://doi.org/10.1007/s13347-012-0093-4
18. Floridi, L. (2016). Mature Information Societies—a Matter of Expectations. Philosophy & Technology, 29(1), 1-4. https://doi.org/10.1007/s13347-016-0214-6
19. Flügge, A. A., Hildebrandt, T.,Møller, N. H. (2021). Street-Level Algorithms and AI in Bureaucratic Decision-Making: A Caseworker Perspective. Proc. ACM Hum.-Comput. Interact., 5(CSCW1), Article 40. https://doi.org/10.1145/3449114
20. Fortes, P. (2021). Hasta la vista, baby: reflections on the risks of algocracy, killer robots, and artificial superintelligence. Revista de la Facultad de Derecho de México, 71(279), 45-72.
21. Ganascia, J.-G. (2010). The generalized sousveillance society. Social Science Information, 49(3), 489-507. https://doi.org/10.1177/0539018410371027
22. Giest, S.,Grimmelikhuijsen, S. (2020). Introduction to special issue algorithmic transparency in government: Towards a multi-level perspective. Information Polity, 25(4), 409-417.
23. Goad, D.,Gal, U. (2018, 2018//). Understanding the Impact of Transparency on Algorithmic Decision Making Legitimacy. Living with Monsters? Social Implications of Algorithmic Phenomena, Hybrid Agency, and the Performativity of Technology, Cham . Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211-221. https://doi.org/10.1007/s10708-007-9111-y Grove, W. M.,Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy. Psychology, public policy, and law, 2(2), 293-323.
24. Hill, J. (2021). Syntegration against the platform: Experimentation with the Team Syntegrity protocol for viable institutional forms that counter the logic of algocracy in institutions of contemporary art Liverpool John Moores University].
25. Hill, R. K. (2016). What an Algorithm Is. Philosophy & Technology, 29(1), 35-59. https://doi.org/10.1007/s13347-014-0184-5
26. Hughes, J. (2017). Algorithms and Posthuman Governance. Journal of Posthuman Studies, 1(2), 166-184. https://doi.org/10.5325/jpoststud.1.2.0166
27. Jain, P., Gyanchandani, M.,Khare, N. (2016). Big data privacy: a technological perspective and review. Journal of Big Data, 3(1), 25. https://doi.org/10.1186/s40537-016-0059-y
28. Janssen, M.,Hoven, v. d. J. (2015). Big and Open Linked Data (BOLD) in government: A challenge to transparency and privacy? Government Information Quarterly, 32(4), 363-368. https://doi.org/https://doi.org/10.1016/j.giq.2015.11.007
29. Janssen, M.,Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance. Government Information Quarterly, 33(3), 371-377. https://doi.org/https://doi.org/10.1016/j.giq.2016.08.011
30. Kariotis, T.,Mir, D. J. (2020). Fighting Back Algocracy: The need for new participatory approaches to technology assessment Proceedings of the 16th Participatory Design Conference 2020 - Participation(s) Otherwise - Volume 2, Manizales, Colombia. https://doi.org/10.1145/3384772.3385151
31. Katzenbach, C.,Ulbricht, L. (2019). Algorithmic governance. Internet Policy Review, 8(4), 1-18.
32. König, P. D. (2020). Dissecting the Algorithmic Leviathan: On the Socio-Political Anatomy of Algorithmic Governance. Philosophy & Technology, 33(3), 467-485. https://doi.org/10.1007/s13347-019-00363-w
33. Krishnan, M. (2020). Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning. Philosophy & Technology, 33(3), 487-502. https://doi.org/10.1007/s13347-019-00372-9
34. Lafont, C. (2020). Against anti-democratic shortcuts: A few replies to critics. Journal of Deliberative Democracy, 16(2), 96-109. https://doi.org/https://doi.org/10.16997/jdd.367
35. Lepri, B., Oliver, N., Letouzé, E., Pentland, A.,Vinck, P. (2018). Fair, Transparent, and Accountable Algorithmic Decision-making Processes. Philosophy & Technology, 31(4), 611-627. https://doi.org/10.1007/s13347-017-0279-x
36. Li, X., Xiong, H., Li, X., Wu, X., Zhang, X., Liu, J., Bian, J.,Dou, D. (2022). Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond. Knowledge and Information Systems, 64(12), 3197-3234. https://doi.org/10.1007/s10115-022-01756-8 Lipton, Z. C. (2015). The myth of model interpretability. Retrieved 14.05.2024 from https://www.kdnuggets.com/2015/04/model-interpretability-neural-networks-deep-learning.html
37. Lorenz, L., Meijer, A.,Schuppan, T. (2021). The algocracy as a new ideal type for government organizations: Predictive policing in Berlin as an empirical case. Information Polity, 26, 71-86. https://doi.org/10.3233/IP-200279
38. Machin, D. J. (2009). The Irrelevance of Democracy to the Public Justification of Political Authority. Res Publica, 15(2), 103-120. https://doi.org/10.1007/s11158-009-9084-4
39. Martin, K.,Waldman, A. (2023). Are Algorithmic Decisions Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of AI Decisions. Journal of Business Ethics, 183(3), 653-670. https://doi.org/10.1007/s10551-021-05032-7
40. Meijer, A., Lorenz, L.,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. https://doi.org/https://doi.org/10.1111/puar.13391
41. Mittelstadt, B. (2017). From Individual to Group Privacy in Big Data Analytics. Philosophy & Technology, 30(4), 475-494. https://doi.org/10.1007/s13347-017-0253-7
42. Monga, V., Li, Y.,Eldar, Y. C. (2021). Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing. IEEE Signal Processing Magazine, 38(2), 18-44. https://doi.org/10.1109/MSP.2020.3016905
43. Moschovakis, Y. N. (2001). What Is an Algorithm? In B. Engquist ve W. Schmid (Ed.), Mathematics Unlimited — 2001 and Beyond (pp. 919-936). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-56478-9_46
44. Muellerleile, C.,Robertson, S. L. (2018). Digital Weberianism: Bureaucracy, Information, and the Techno-rationality of Neoliberal Capitalism. Indiana Journal of Global Legal Studies, 25(1), 187-216. https://doi.org/10.2979/indjglolegstu.25.1.0187
45. Neves, I. B. S., Vianna, F. R. P. M.,Sutil, B. d. N. (2021). Algocracy: A critical analysis on management mediated by algorithms. Contextus – Revista Contemporânea de Economia e Gestão, 19(0), 246-256. https://doi.org/10.19094/contextus.2021.67949 Newell, B. C. (2014). Technopolicing, surveillance, and citizen oversight: A neorepublican theory of liberty and information control. Government Information Quarterly, 31(3), 421-431. https://doi.org/https://doi.org/10.1016/j.giq.2014.04.001
46. Pääkkönen, J., Nelimarkka, M., Haapoja, J.,Lampinen, A. (2020). Bureaucracy as a lens for analyzing and designing algorithmic systems. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems,
47. Peeters, R.,Schuilenburg, M. (2018). Machine justice: Governing security through the bureaucracy of algorithms. Information Polity, 23, 267-280. https://doi.org/10.3233/IP-180074
48. Price, W. N.,Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0272-7
49. Roche, S., Nabian, N., Kloeckl, K.,Ratti, C. (2012). Are ‘smart cities’ smart enough. Global geospatial conference, Roehl, U.,Crompvoets, J. (2023). Inside algorithmic bureaucracy: Disentangling automated decision-making and good administration. Public Policy and Administration, 0(0), 09520767231197801. https://doi.org/10.1177/09520767231197801
50. Rudin, C. (2014). Algorithms for interpretable machine learning Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, New York, USA. https://doi.org/10.1145/2623330.2630823
51. Sætra, H. S. (2020). A shallow defence of a technocracy of artificial intelligence: Examining the political harms of algorithmic governance in the domain of government. Technology in Society, 62, 101283. https://doi.org/https://doi.org/10.1016/j.techsoc.2020.101283
52. Sandvig, C. (2014). Seeing the Sort: The Aesthetic and Industrial Defense of “The Algorithm”. http://median.newmediacaucus.org/art-infrastructures-information/seeing-the-sort-the-aesthetic-and-industrial-defense-of-the-algorithm/
53. Sayers, J. (2016). ComputerVision as a Public Act: On Digital Humanities and Algocracy. Disrupting the Digital Humanities. https://www.disruptingdh.com/computer-vision-as-a-public-act-on-digital-humanities-and-algocracy/
54. Soria-Comas, J.,Domingo-Ferrer, J. (2016). Big Data Privacy: Challenges to Privacy Principles and Models. Data Science and Engineering, 1(1), 21-28. https://doi.org/10.1007/s41019-015-0001-x
55. Stahl, B. C.,Wright, D. (2018). Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation. IEEE Security & Privacy, 16(3), 26-33. https://doi.org/10.1109/MSP.2018.2701164
56. Tagiew, R. (2020). Roadmap to algocracy-a feasibility study. Available at SSRN 3650010. Victor, N., Lopez, D.,Abawajy, J. H. (2016). Privacy models for big data: a survey. International Journal of Big Data Intelligence, 3(1), 61-75.
57. Vogl, T. M., Seidelin, C., Ganesh, B.,Bright, J. (2020). Smart Technology and the Emergence of Algorithmic Bureaucracy: Artificial Intelligence in UK Local Authorities. Public Administration Review, 80(6), 946-961. https://doi.org/https://doi.org/10.1111/puar.13286
58. Xu, L., Jiang, C., Wang, J., Yuan, J.,Ren, Y. (2014). Information Security in Big Data: Privacy and Data Mining. IEEE Access, 2, 1149-1176. https://doi.org/10.1109/ACCESS.2014.2362522
59. Zarsky, T. Z. (2011). Automated prediction: perception, law, and policy. Communications of the ACM, 55(9), 33–35. https://doi.org/10.1145/2330667.2330678
60. Zerilli, J., Knott, A., Maclaurin, J.,Gavaghan, C. (2019). Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard? Philosophy & Technology, 32(4), 661-683. https://doi.org/10.1007/s13347-018-0330-6
61. Zheng, S., Trott, A., Srinivasa, S., Naik, N., Gruesbeck, M., Parkes, D. C.,Socher, R. (2020). The ai economist: Improving equality and productivity with ai-driven tax policies. arXiv preprint arXiv:2004.13332.
62. Zuurmond, A. (2005). Organisational Transformation through the Internet. Journal of Public Policy, 25(1), 133-148. http://www.jstor.org/stable/4007788