No Algorithmization Without Representation: Pilot Study on Regulatory Experiments in an Exploratory Sandbox
Status PubMed-not-MEDLINE Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články
PubMed
36237447
PubMed Central
PMC9341158
DOI
10.1007/s44206-022-00002-6
PII: 2
Knihovny.cz E-zdroje
- Klíčová slova
- Algogovernance, Contact tracing, Experimental governance, Exploratory sandbox,
- Publikační typ
- časopisecké články MeSH
The exploratory sandbox for blockchain services, Lithopy, provided an experimental alternative to the aspirational frameworks and guidelines regulating algorithmic services ex post or ex ante. To understand the possibilities and limits of this experimental approach, we compared the regulatory expectations in the sandbox with the real-life decisions about an "actual" intrusive service: contact tracing application. We gathered feedback on hypothetical and real intrusive services from a group of 59 participants before and during the first and second waves of the COVID-19 pandemic in the Czech Republic (January, June 2020, and April 2021). Participants expressed support for interventions based on an independent rather than government oversight that increases participation and representation. Instead of reducing the regulations to code or insisting on strong regulations over the code, participants demanded hybrid combinations of code and regulations. We discuss this as a demand for "no algorithmization without representation." The intrusive services act as new algorithmic "territories," where the "data" settlers must redefine their sovereignty and agency on new grounds. They refuse to rely upon the existing institutions and promises of governance by design and seek tools that enable engagement in the full cycle of the design, implementation, and evaluation of the services. The sandboxes provide an environment that bridges the democratic deficit in the design of algorithmic services and their regulations.
Centre for Distributed Ledger Technologies University of Malta Msida Malta
University of West Bohemia in Pilsen Zapadoceska Univerzita 5 Plzni Pilsen Czech Republic
Zobrazit více v PubMed
Alaassar A, Mention AL, Aas TH. Exploring how social interactions influence regulators and innovators: The case of regulatory sandboxes. Technological Forecasting and Social Change. 2020;160:120257. doi: 10.1016/j.techfore.2020.120257. PubMed DOI
Alexander L. What makes wrongful discrimination wrong? Biases, preferences, stereotypes, and proxies. University of Pennsylvania Law Review. 1992;141(1):149. doi: 10.2307/3312397. DOI
Awad E, Dsouza S, Kim R, Schulz J, Henrich J, Shariff A, Bonnefon J-F, Rahwan I. The Moral Machine Experiment. 2018 doi: 10.1038/s41586-018-0637-6. PubMed DOI
Aziz. (n.d.). Guide to forks: Everything you need to know about forks, hard fork and soft fork. 2020. Retrieved January 21, 2020, from https://masterthecrypto.com/guide-to-forks-hard-fork-soft-fork/?lang=en
Begby E. Automated risk assessment in the criminal justice process. Prejudice. 2021 doi: 10.1093/OSO/9780198852834.003.0009. DOI
Bromberg L, Godwin A, Ramsay I. Cross-border cooperation in financial regulation: Crossing the Fintech bridge. Capital Markets Law Journal. 2018;13(1):59–84. doi: 10.1093/cmlj/kmx041. DOI
Burke, A. (2019, July 1). Occluded Algorithms. Big Data & Society, 6(2), 2053951719858743. 10.1177/2053951719858743
Carter, P. (2000). Men and the emergence of polite society, Britain, 1660–1800 | Reviews in History. Longman. https://reviews.history.ac.uk/review/195
Cavoukian A. Privacy by Design - The 7 foundational principles - Implementation and mapping of fair information practices. Information and Privacy Commissioner of Ontario, Canada. 2009 doi: 10.1007/s12394-010-0062-y. DOI
Chwalisz C. Reimagining democratic institutions: Why and how to embed public deliberation. Innovative Citizen Participation and New Democratic Institutions. 2020 doi: 10.1787/339306DA-EN. DOI
Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., De Paor, A., Felzmann, H., et al. (2017). Algorithmic Governance: Developing a Research Agenda through the Power of Collective Intelligence. Big Data & Society, 4(2), 2053951717726554. 10.1177/2053951717726554
De Filippi, P., & Hassan, S. (2018). Blockchain technology as a regulatory technology from code is law to law is code. In arXiv.
Elkin-Koren N. Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence. Big Data & Society. 2020;7(2):205395172093229. doi: 10.1177/2053951720932296. DOI
Fan, P. S. (2017). Singapore approach to develop and regulate FinTech. In Handbook of Blockchain, Digital Finance, and Inclusion, Volume 1: Cryptocurrency, FinTech, InsurTech, and Regulation. 10.1016/B978-0-12-810441-5.00015-4
Financial Conduct Authority, & Authority, F. C. (2015). Regulatory sandbox. Fca, November, 26. 10.1111/j.1740-8261.2011.01810.x
Fitsilis F. Imposing regulation on advanced algorithms. Springer International Publishing. 2019 doi: 10.1007/978-3-030-27979-0. DOI
Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, Madelin R, Pagallo U, Rossi F, Schafer B, Valcke P, Vayena E. AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines. 2018;28(4):689–707. doi: 10.1007/s11023-018-9482-5. PubMed DOI PMC
Goodman, B., & Flaxman, S. (2017). European Union Regulations on Algorithmic Decision-Making and a ‘Right to Explanation. AI Magazine, 38(3), 50–57. 10.1609/aimag.v38i3.2741
Gromova E, Ivanc T. Regulatory sandboxes (Experimental legal regimes) for digital innovations in brics. BRICS Law Journal. 2020;7(2):10–36. doi: 10.21684/2412-2343-2020-7-2-10-36. DOI
Hajian, S., Bonchi, F., & Castillo, C. (2016). Algorithmic bias: From discrimination discovery to fairness-aware data mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17-August-2016, pp. 2125–2126. 10.1145/2939672.2945386
Hee-jeong Choi, J., Forlano, L., & Reshef Kera, D. (2020). Situated automation. Proceedings of the 16th Participatory Design Conference 2020 - Participation(s) Otherwise - Volume 2, 5–9. 10.1145/3384772.3385153
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The Weirdest People in the World?” Behavioral and Brain Sciences, 33(2–3), 61–83. 10.1017/S0140525X0999152X PubMed
Herrera, D., & Vadillo, S. (2018). Regulatory sandboxes in Latin America and the Caribbean for the FinTech Ecosystem and the Financial System.
Hildebrandt, M. (2018). Algorithmic Regulation and the Rule of Law.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), 20170355. 10.1098/rsta.2017.0355 PubMed
Introna, L. D. (2016). Algorithms, governance, and governmentality: On governing academic writing. Science Technology and Human Values, 41(1). 10.1177/0162243915587360
Johnson GM. Algorithmic bias: on the implicit biases of social technology. Synthese. 2020;198(10):9941–9961. doi: 10.1007/S11229-020-02696-Y. DOI
Kenety, B. (2020). Over 1 million Czechs download eFacemask app, but many fear ‘Big Brother’ is watching | Radio Prague International. Aktualne.Cz.
Kera, D. R. (2021). Exploratory RegTech: Sandboxes supporting trust by balancing regulation of algorithms with automation of regulations. In M. H. ur Rehman, D. Svetinovic, K. Salah, & E. Damiani (Eds.), Trust Models for Next-Generation Blockchain Ecosystems (pp. 67–84). Springer International Publishing. 10.1007/978-3-030-75107-4_3
Khanna, P. (2012). The rise of hybrid governance. McKinsey & Co. Insights & Publications.
Kroll, J., Huey, J., & Barocas, S, Felten, E., Reidenberg, J., Robinson, D., & Yu, H. (2017). Accountable Algorithms. University of Pennsylvania Law Review, 165(3).
Lee, M. K., Kusbit, D., Kahng, A., Kim, J. T., Yuan, X., Chan, A., See, D. et al. (2019, November 7). WeBuildAI: Participatory Framework for Algorithmic Governance. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–35. 10.1145/3359283. (January 1, 2017):633.
Madir, J., Lim, B., & Low, C. (2019). Regulatory sandboxes. In FinTech (pp. 302–325). Edward Elgar Publishing. 10.4337/9781788979023.00028
Mulligan DK, Bamberger KA. Saving governance-by-design. California Law Review. 2018;106(3):697–784. doi: 10.15779/Z38QN5ZB5H. DOI
Nemcova, J. (2021). eRouška nefunguje, nainstalovalo si ji málo lidí. Při trasování nepomáhá, říkají hygienici | iROZHLAS - spolehlivé zprávy. IRozhlas.
Rahwan I. Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology. 2018;20(1):5–14. doi: 10.1007/s10676-017-9430-8. DOI
Reshef Kera, D. (2020a). Experimental algorithmic citizenship in the sandboxes: An alternative to ethical frameworks and governance- by-design interventions. In C. Meza, L. Hernández-Callejo, S. Nesmachnow, Â. Ferreira, & V. Leite (Eds.), Proceedings of the III Ibero-American Conference on Smart Cities (ICSC-CITIES2020a) (pp. 29–43). Instituto Tecnológico de Costa Rica. https://www.researchgate.net/publication/351148723_Experimental_Algorithmic_Citizenship_in_the_Sandboxes_an_Alternative_to_Ethical_Frameworks_and_Governance-_by-Design_Interventions
Reshef Kera, D. (2020b). Sandboxes and testnets as “trading zones” for blockchain governance. In Advances in Intelligent Systems and Computing: Vol. 1238 AISC. 10.1007/978-3-030-52535-4_1
Reshef Kera, D. (2020c). Sandboxes and testnets as “trading zones” for blockchain governance. 3–12. 10.1007/978-3-030-52535-4_1
Reshef Kera D. Anticipatory policy as a design challenge: Experiments with stakeholders engagement in blockchain and distributed ledger technologies (bdlts) Advances in Intelligent Systems and Computing. 2020;1010:87–92. doi: 10.1007/978-3-030-23813-1_11. DOI
Reshef Kera D, Kraiński M, Rodríguez JMC, Sčourek P, Reshef Y, Knoblochová IM. Lithopia: Prototyping blockchain futures. Conference on Human Factors in Computing Systems - Proceedings. 2019 doi: 10.1145/3290607.3312896. DOI
Roio, D. (2018). Algorithmic sovereignty. University of Plymouth.
Rouvroy A. The end(s) of critique: Data behaviourism versus due process. Undefined. 2013 doi: 10.4324/9780203427644-16. DOI
Sabel CF, Zeitlin J. Experimentalist Governance. 2012 doi: 10.1093/OXFORDHB/9780199560530.013.0012. DOI
Shilton K. Engaging values despite neutrality. Science, Technology, & Human Values. 2018;43(2):247–269. doi: 10.1177/0162243917714869. DOI
Shneiderman, B. (2016, November 29). The Dangers of Faulty, Biased, or Malicious Algorithms Requires Independent Oversight. Proceedings of the National Academy of Sciences, 113(48), 13538–13540. 10.1073/pnas.1618211113 PubMed PMC
Shorey S, Howard PN. Automation, big data, and politics: A research review. International Journal of Communication. 2016;10:5032–5055.
Sloane, M., Moss, E., Awomolo, O., & Forlano, L. (2020). Participation is not a design fix for machine learning.
Susskind, J. (2018). Future politics. living together in a world transformed by tech. Oxford University Press, 516.
Vili Lehdonvirta. (2016). The blockchain paradox: Why distributed ledger technologies may do little to transform the economy. Oxford Internet Institute Blog. https://www.oii.ox.ac.uk/blog/the-blockchain-paradox-why-distributed-ledger-technologies-may-do-little-to-transform-the-economy/
Winfield, A. F. T., & Jirotka, M. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133). 10.1098/rsta.2018.0085 PubMed PMC
Yeung K. Algorithmic regulation: A critical interrogation. Regulation and Governance. 2018;12(4):505–523. doi: 10.1111/rego.12158. DOI
Zeitlin, J. (2017). Extending experimentalist governance?: The European Union and transnational regulation. Oxford University Press.