Abstract
This contribution addresses links between machine learning technologies and democracy with a focus on political participation. Democracy research often regards machine learning technologies as a threat, as these technologies could violate fundamental rights or replace democratic decision making. While raising important concerns, these approaches underestimate the malleability of digital technologies and their relationship to democracy. Our argument is that inherent to democratic practice we find a constant (re)negotiation of rights and institutions, in this case not least driven by the fact that machine learning technologies themselves are far from reaching maturity. The openness and negotiability of the relationship of AI and democracy is illustrated by three critical perspectives that hold special importance for political participation: algorithmic bias, automated decision-making and AI’s epistemic dimension. By reflecting the changing condition of political organisation, current research can be productive and even performative in the sense of co-defining a shared understanding of new technologies and aim to set standards for their legitimate use.
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Notes
- 1.
As we demonstrate with examples throughout this text.
- 2.
Although or precisely because artificial intelligence (AI) is currently a much-discussed topic, it is difficult to define it. For this reason, many experts completely abandon the term and switch to abstractions such as “predictive technologies”, “agentic machines”, or “algorithmic systems” (Joyce et al. 2021, p. 2; see also Dignum 2022). While this leads some observers to question the very existence of AI, others prefer to reserve the term for “whatever we are doing next in computing” (Recker et al. 2021, p. 1435). Nevertheless, the vague terminology proves to be a problem if one wants to investigate the interactions of AI with society and democracy. To avoid ambiguity, we refer instead to algorithmic systems, learning algorithms, or machine learning.
- 3.
Legal philosophy and behavioural theories have explored an array of factors that judges weigh into this process of materializing what are, ultimately, ethical standards elected by the rule of law (Pereira 2016, p. 347), including hermeneutic choices, political implications, and institutional culture, beyond personal ideologies and bias (Campos Mello 2018; Pereira 2016; Freitas 2018).
- 4.
For instance, an experiment in the US Supreme Court led by Epstein et al. (2018, p. 239) found that justices who subscribe to a liberal ideology were more supportive of free-speech claims than conservative justices. Showing that bias also infiltrates collegiate deliberation, Cesário Alvim Gomes et al. (2018) documented how justices of the Brazilian Supreme Court were found more likely to disagree with rulings reported by female justices, in comparison to their male peers (Cesário Gomes Alvim, Werneck Arguelhes, und Nogueira 2018, p. 866).
- 5.
For hermeneutic techniques, see Freitas 2018.
- 6.
There are options to choose from when selecting learning algorithms defining target variables, compiling training and test data as well as optimizing during training processes (cf. Domingos 2012, p. 79–80).
- 7.
In recent years, the increasing use of automated decision-making systems has not only brought existing network policy organizations onto the scene but has also led to a number of start-ups: NGOs such as the Ada Lovelace Institute (2018, UK), Algorithm watch (2017, Germany), AI Now (2017, USA) or Data and Society (2014, USA.).
- 8.
COM (2021) 206 final.
- 9.
Hartmut Rosa (2020, p. 21) describes subjecting the world to control along four dimensions: recognizability, accessibility, controllability and usability.
References
Abebe, R., Barocas, S., Kleinberg, J., Levy, K., Raghavan, M., and Robinson, D. G. 2020. Roles for Computing in Social Change. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, arXiv:1912.04883v4 [cs.CY], 252–260. https://doi.org/10.1145/3351095.3372871
Ada Lovelance Institute, AI Now Institute, and Open Government Partnership. 2021. Algorithmic accountability for the public sector. Learning from the first wave of policy implementation. https://www.opengovpartnership.org/documents/algorithmic-accountability-public-sector/
AlgorithmWatch, and Bertelsmann Stiftung. 2020. Automating Society Report 2020. AlgorithmWatch, Bertelsmann Stiftung. https://automatingsociety.algorithmwatch.org/wp-content/uploads/2021/01/Automating_Society_Report_2020_-_Deutsche_Ausgabe.pdf
Amoore, L. 2020. Cloud Ethics. Algorithms and the Attributes of Ourselves and Others. Duke University Press.
Balkin, J. M. 2014. Old School/New School Speech Regulation. Yale Law School, Public Law Rearch Paper, 491. https://ssrn.com/abstract=2377526
Bennett, W. L., Segerberg, A., and Knüpfer, C. B. 2018. The democratic interface: Technology, political organization, and diverging patterns of electoral representation. Information, Communication and Society, 21(11), 1655–1680. https://doi.org/10.1080/1369118X.2017.1348533
Binns, R. 2020. Human Judgment in algorithmic loops: Individual justice and automated decision‐making. Regulation and Governance, 16(1), 197–211. https://doi.org/10.1111/rego.12358
Block, K., and Dickel, S. 2020. Jenseits der Autonomie: Die De/Problematisierung des Subjekts in Zeiten der Digitalisierung. BEHEMOTH – A Journal on Civilisation, 13(1), 109–131. https://doi.org/10.6094/behemoth.2020.13.1.1040
Bryson, J. J. 2022, March 2. Europe Is in Danger of Using the Wrong Definition of AI. WIRED. https://www.wired.com/story/artificial-intelligence-regulation-european-union/
Bucher, T. 2018. If … Then: Algorithmic Power and Politics. Oxford University Press.
Campos Mello, P. P. 2018. A vida como ela é’: comportamento estratégico nas cortes. Revista Brasileira de Políticas Públicas 8(2). https://doi.org/10.5102/rbpp.v8i2.5481
Cesário Gomes Alvim, J., Werneck Arguelhes, D., and Nogueira, R. 2018. Gênero e comportamento judicial no supremo tribunal federal: Os ministros confiam menos em relatoras mulheres? Revista Brasileira de Políticas Públicas 8(2). https://doi.org/10.5102/rbpp.v8i2.5326
Dignum, V. 2022. Relational Artificial Intelligence. arXiv:2202.07446 [cs.CY]. https://doi.org/10.48550/arXiv.2202.07446
Djeffal, C. 2019. AI, Democracy and the Law. In Andreas Sudmann (ed.), The Democratization of Artificial Intelligence (pp. 255–284). transcript Verlag. https://doi.org/10.1515/9783839447192-016
Domingos, P. 2012. A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87. https://doi.org/10.1145/2347736.2347755
EDRi, Access Now, epicenter.works, AlgorithmWatch, EDF, Bits of Freedom, Fair Trials, PICUM, ANEC, and Panoptykon Foundation. 2021. An EU Artificial Intelligence Act for Fundamental Rights. A Civil Society Statement. European Digital Rights. https://edri.org/wp-content/uploads/2021/12/Political-statement-on-AI-Act.pdf
Edwards, L. 2022. Regulating AI in Europe: Four problems and four solutions. Ada Lovelace Institute. https://www.adalovelaceinstitute.org /report/regulating- ai-in-europe/
Edwards, L., and Veale, M. 2017. Slave to the Algorithm? Why a “Right to an Explanation“ Is Probably Not the Remedy You Are Looking For (SSRN Scholarly Paper Nr. 2972855). Social Science Research Network. https://doi.org/10.2139/ssrn.2972855
Epstein, L., Parker, C. M., and Segal, J. A. 2018. Do Justices Defend the Speech They Hate? An Analysis of In-Group Bias on the US Supreme Court. Journal of Law and Courts 6(2): 237–262. https://doi.org/https://doi.org/10.1086/697118
Eubanks, V. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St Martin’s Press.
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., and Srikumar, M. 2020. Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3518482
Fourcade, M., and Johns, F. 2020. Loops, ladders and links: The recursivity of social and machine learning. Theory and Society, 49(5), 803–832. https://doi.org/10.1007/s11186-020-09409-x
Freitas, J. 2018. Interpretação Judicial: Exame Crítico Dos Vieses. Revista Da AJUFERGS 10(1a): 57–84.
Fry, H. 2019. What Statistics Can and Can’t Tell Us About Ourselves. The New Yorker, 2. September 2019. https://www.newyorker.com/magazine/2019/09/09/what-statistics-can-and-cant-tell-us-about-ourselves
Habermas, J., Sperber, G. B., and Soethe, P. 2007. A inclusão do outro: estudos de teoria política (3rd ed., Humanística 3). Loyola.
Habermas, J. 1996. Between Facts and Norms: Contributions to a Discourse Theory of Law and Democracy. Studies in Contemporary German Social Thought. Cambridge, Mass: MIT Press.
Harari, Y. N. 2018. Why technology favors tyranny. The Atlantic. https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/
Hayles, K. 2005. Computing the Human. Theory Culture and Society, 22(1), 131–151. https://doi.org/https://doi.org/10.1177/0263276405048438
Heintz, B. 2021. Big Observation – Ein Vergleich moderner Beobachtungsformate am Beispiel von amtlicher Statistik und Recommendersystemen. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 73(1), 137–167. https://doi.org/https://doi.org/10.1007/s11577-021-00744-0
High-Level Expert Group on Artificial Intelligence. 2018. Ethics guidelines for trustworthy AI. European Commission. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
Hildebrandt, M. 2016. Law as Information in the Era of Data-Driven Agency. The Modern Law Review, 79(1), 1–30. https://doi.org/10.1111/1468-2230.12165
Hofmann, J. 2019. Mediated Democracy – Linking Digital Technology to Political Agency. Internet Policy Review 8(2). https://doi.org/10.14763/2019.2.1416
Joyce, K., Smith-Doerr, L., Alegria, S., Bell, S., Cruz, T., Hoffman, S. G., Noble, S. U., and Shestakofsky, B. 2021. Toward a Sociology of Artificial Intelligence: A Call for Research on Inequalities and Structural Change. Socius, 7, 1–11. https://doi.org/10.1177/2378023121999581
Kahlert, P. 2022, Februar 28. YouTubes Wahl. STS@ENS. https://medium.com/sts-ens/youtubes-wahl-ea9c4df1297e
Kahneman, D. 2003. Maps of Bounded Rationality: Psychology for Behavioral Economics. The American Economic Review, 93(5), 1449–1475.
Kayser-Bril, N. 2021, August 13. Nach Drohungen von Facebook: AlgorithmWatch sieht sich gezwungen, Instagram-Forschungsprojekt einzustellen. AlgorithmWatch. https://algorithmwatch.org/de/instagram-forschung-von-facebook-gestoppt/
Keller, P., and Drake, A. 2022, March 30. Proactive Contestation of AI Decision-making. Verfassungsblog. https://verfassungsblog.de/roa-proactive-contestation-of-ai-decision-making/
König, P. D., and Wenzelburger, G. 2020. Opportunity for renewal or disruptive force? How artificial intelligence alters democratic politics. Government Information Quarterly, 37(3). https://doi.org/10.1016/j.giq.2020.101489
Koster, A.-K. 2021. Das Ende des Politischen? Demokratische Politik und Künstliche Intelligenz. Zeitschrift für Politikwissenschaft. https://doi.org/10.1007/s41358-021-00280-5
Mackenzie, A. 2017. Machine Learners: Archaeology of a Data Practice. Cambridge, MA: The MIT Press. https://doi.org/https://doi.org/10.7551/mitpress/10302.001.0001
Makropoulos, M. 2021. Historische Kontingenzen und soziale Optimierung. In. Bubner, M. und Mesch, W. (Hrsg.) Die Weltgeschichte – das Weltgericht? Stuttgarter Hegel-Kongreß 1999 22: 75–90. Veröffentlichungen der Internationalen Hegel-Vereinigung. Stuttgart: Klett-Cotta. https://www.researchgate.net/publication/356446646_Digital_Media_and_Democracy_A_Systematic_Review_of_Causal_and_Correlational_Evidence_Worldwide
McQuillan, D. 2018. “People’s Councils for Ethical Machine Learning.” Social Media + Society 4(2): 1–10. https://doi.org/10.1177/2056305118768303
Mol, A. 1999. Ontological Politics. A Word and Some Questions. The Sociological Review 47(1): 74–89. https://doi.org/10.1111/j.1467-954X.1999.tb03483.x
O’Neil, C. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Allen Lane.
Panagia, D. 2021. On the Possibilities of a Political Theory of Algorithms. Political Theory, 49(1), 109–133. https://doi.org/10.1177/0090591720959853
Pereira, J. R. G. 2016. As Garantias Constitucionais Entre Utilidade e Substância. Revista Brasileira de Direitos Fundamentais and Justiça 10(35): 345–373. https://doi.org/10.30899/dfj.v10i35.105.
Recker, J., Berente, N., Santanam, R., and Gu, B. 2021. Managing Artificial Intelligence. MIS Quarterly, 45, 1433–1450. https://doi.org/10.25300/MISQ/2021/16274
Rieder, B., and Hofmann, J. 2020. Towards platform observability. Internet Policy Review, 9(4), 1–28. https://doi.org/10.14763/2020.4.1535
Rieder, B., and Skop, Y. 2021. The fabrics of machine moderation: Studying the technical, normative, and organizational structure of Perspective API. Big Data and Society, 8(2), 1–16. https://doi.org/https://doi.org/10.1177/20539517211046181
Rosa, H. 2020. Unverfügbarkeit. Wien. Residenzverlag.
Rostalski, F., and Thiel, T. 2021. Künstliche Intelligenz als Herausforderung für demokratische Partizipation. In Interdisziplinäre Arbeitsgruppe „Verantwortung: Maschinelles Lernen und Künstliche Intelligenz“ der Berlin-Brandenburgischen Akademie der Wissenschaften (Hrsg.), Verantwortungsvoller Einsatz von KI? Mit menschlicher Kompetenz! (pp. 56–63). Berlin-Brandenburgische Akademie der Wissenschaften. http://hdl.handle.net/10419/235149
de Sousa Santos, Boaventura. 2002. Reinventar a democracia (2nd ed.). Ed. Gradiva.
Savaget, P., Chiarini, T. and Evans, S. 2019. Empowering Political Participation through Artificial Intelligence. Science and Public Policy 46(3): 369–380. https://doi.org/https://doi.org/10.1093/scipol/scy064
Scheuerman, M. K., Denton, E., and Hanna, A. 2021. Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2, 317), 1–37. https://doi.org/10.1145/3476058
Schippers, B. 2020. Artificial Intelligence and Democratic Politics. Political Insight, 11(1): 32–35. https://doi.org/10.1177/2041905820911746.
Selbst, A. D., Boyd, D., Friedler, S., Venkatasubramanian, S., and Janet Vertesi. 2018. Fairness and Abstraction in Sociotechnical Systems (SSRN Scholarly Paper Nr. 3265913). Social Science Research Network. https://papers.ssrn.com/abstract=3265913
Shneiderman, B. 2020. Human-Centered Artificial Intelligence: Reliable, Safe and Trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
Thiel, T. 2022, April 19. Artificial Intelligence and Democracy. Israel Public Policy Institute. https://www.ippi.org.il/artificial-intelligence-and-democracy/
Ulbricht, L. 2020. Scraping the demos. Digitalization, web scraping and the democratic project. Democratization, 27(3), 426–442. https://doi.org/10.1080/13510347.2020.1714595
Viljoen, S. 2021. A Relational Theory of Data Governance. The Yale Law Journal, 82.
Waldron, J. 2008. Judges as Moral Reasoners. International Journal of Constitutional Law, 7(1): 2–24. https://doi.org/10.1093/icon/mon035.
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Hofmann, J., Iglesias Keller, C. (2024). Machine learning, political participation and the transformations of democratic self-determination. In: Heinlein, M., Huchler, N. (eds) Künstliche Intelligenz, Mensch und Gesellschaft. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-43521-9_13
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