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The E.U.’s artificial intelligence act: an ordoliberal assessment

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Abstract

In light of the rise of generative AI and recent debates about the socio-political implications of large-language models and chatbots, this article investigates the E.U.’s artificial intelligence act (AIA), the world’s first major attempt by a government body to address and mitigate the potentially negative impacts of AI technologies. The article critically analyzes the AIA from a distinct economic ethics perspective, i.e., ‘ordoliberalism 2.0’—a perspective currently lacking in the academic literature. It evaluates, in particular, the AIA’s ordoliberal strengths and weaknesses and proposes reform measures that could be taken to strengthen the AIA.

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Notes

  1. Some critics have backed their criticism with (a call for) action: Hinton, for example, has resigned from Google and warns of the dangers of AI [9], Musk et al. have signed an open letter demanding the pause of AI development [10], see Gebru et al. [6, 7], Altman calls for AI (self-)regulation to mitigate the ‘risks of increasingly powerful AI’ [11], the Center for AI Safety released a statement which warns that AI poses a severe ‘risk of extinction’ and could be as deadly as pandemics and nuclear weapons (the statement received widespread support from leading AI companies and scientists) [5, 12], and other researchers and politicians argue that global governance and an international AI agency are needed (i.e., ‘IAEA for AI’) [13,14,15].

  2. Eucken’s Constituent Principles include (1) competitive market order; (2) primacy of monetary policy/price stability; (3) open markets; (4) private property rights; (5) freedom of contracts; (6) principle of liability; (7) long-term orientation of economic policy; and (8) interdependency of all Constituent Principles [45]. Eucken’s Regulating Principles include (1) correction of market powers; (2) income redistribution; (3) correction of negative external effects; and (4) correction of ‘abnormal supply reactions’ [27, 45].

  3. According to Wörsdörfer [27], ‘ordoliberalism 2.0’ rests on the following principles: competitive economy, open markets, freedom of contract/liability, correction of market power, limiting rent-seeking, regulatory and competition policy, rule of law, freedom of privileges/non-discrimination, subsidiarity, and correction of negative external effects.

  4. The AIA’s strengths include its legally-binding, i.e., hard-law, character [81, 82], which marks a welcoming departure from existing soft-law AI ethics initiatives [17, 39, 59, 83,84,85,86,87,88,89,90], extra-territoriality and possible extension of the ‘Brussels Effect’ [91,92,93], ability to address data quality and discrimination risks [75], and institutional innovations such as the EAIB and publicly accessible logs/database for AI systems (as an essential step in opening up black-box algorithms) [94]. From a (revised ordoliberal perspective, it is worth pointing out that the AIA attempts to ensure that AI technologies are ‘ethically sound, legally acceptable, socially equitable, and environmentally sustainable, with a[n] [ordoliberal] vision of AI that seeks to support [i.e., serve] the economy, society, and the environment’ [92]. Note that Röpke, Rüstow, and other ordoliberals also believed that the economy is embedded in a higher societal order—‘beyond supply and demand.’ Its primary purpose is to serve the people and society, not vice versa. It is thus seen as a means to an end, not as an end in itself (the ordoliberal end in itself is the so-called ‘vital situation’ or ‘private law society’). They also believed that the economy drains and erodes morality,‘moral reserves’ thus need to be built outside the economy, i.e., in ‘market-free’ sectors and with the help of ‘vital policy’ [29, 34, 48, 49, 49, 52, 78, 95, 95,96,97,98].

  5. The AIA’s weaknesses relate to its tendency to prioritize economic, business, and innovation over moral concerns (i.e., de-prioritization of human rights) [67, 68, 92, 99,100,101,102], the lack of a clear definition of AI systems (i.e., lack of scope) [80, 84, 94, 101, 103], the flawed risk-based framework (i.e., incomplete list of prohibited AI systems and under-regulation of non-high-risk AI systems) [84, 94, 100, 101, 104,105,106,107,108], and the failure to adequately address the challenges posed by generative AI.

  6. Standardization bodies such as CEN and CENELEC are responsible for drafting harmonized voluntary technical standards, e.g., in the areas of electrical engineering.

  7. Notified bodies such as TÜV and other technical organizations are accredited by member states’ notifying authorities and are responsible for assessing and verifying the conformity assessment procedure.

  8. The AIA, for instance, encourages providers of non-high-risk AI systems to voluntarily apply the mandatory requirements for high-risk AI systems laid out in Title III. It also urges providers to voluntarily commit themselves—via codes of conduct—to environmental sustainability, accessibility for persons with disability, stakeholder participation, and team diversity.

  9. Besides input legitimacy (i.e., a lack of stakeholder consultation and participation), those processes might also lack throughput (i.e., a lack of accountable and transparent processes) and output legitimacy (i.e., a lack of standard responsiveness, e.g., about the interests of affected stakeholder groups) [115].

  10. Note that the Parliament has no binding veto power over harmonized standards mandated by the Commission.

  11. Note that Eucken and other ordoliberals saw unions as an essential counterweight to the power of employers [45, 46].

  12. Process policy is rejected for several reasons: It is considered by Eucken and other ordoliberals as a form of ‘privilege-granting policy.’ It is mainly based on ad-hoc and case-by-case decisions and enables arbitrary and selective interventions in the economic ‘game of catallaxy,’ to use Hayek’s [117] term. It thus lacks two crucial features of an ordoliberal economic policy—predictability and long-term orientation. Most importantly, however, it opens the doors for special interest groups to exert influence on the legislative decision-making process: That is, process policy is more likely to be prone to the power of rent-seeking or lobbying groups—due to a more significant regulatory load and the existence of a higher discretionary leeway for decision-making. It thus goes hand in hand with a considerable lack of transparency as many debates and decisions take place behind closed doors—and a lack of accountability and democratic legitimacy—since interest groups represent only a fraction of society and are seldom directly and democratically elected (besides, process policy also tends to weaken or undermine constitutional checks and balances). In sum, this form of particularistic policy jeopardizes the nation’s wealth—due to granting costly and exclusive privileges to special interest groups—and undermines personal freedom—due to the increased politico-economic powers of rent-seekers.

  13. Note that Sect. 5 mirrors the previous section, i.e., the reform measures introduced in this section address the concerns raised in Sect. 4 in the exact same order (e.g., the paragraph on independent conformity assessment addresses the lack of enforcement concern in the previous section, and so on).

  14. According to AlgorithmWatch [94], it is also hardly justifiable to leave the assessment of societal risks and impacts to corporate (i.e., for-profit) actors and their self-interests (i.e., profit and shareholder value maximization).

  15. Based on the notion of ‘predetermined change,’ anticipated AI system modifications currently do not trigger a new conformity assessment [67].

  16. Stuurman and Lachaud [108] argue for introducing mandatory AI labeling schemes for secure, responsible, and ethical AI systems. They claim that the current CE marking process (alone) is insufficient due to its lack of clarity, trust, monitoring, and transparency. Those labeling schemes could be similar to information or nutrition labels; that is, they would provide information about the goal of the AI system, the data collected and processed, and contact information to send queries and file complaints. The AI Ethics Label initiative suggests evaluating six dimensions of AI systems (transparency, accountability, privacy, justice/non-discrimination, reliability, and sustainability on a scale from A to G under a graphical design similar to the European energy efficiency label). The AI label would utilize ex-ante audits to validate the provided information, and such labels would need to be renewed regularly, given machine-learning progress (see for foundation [120, 121].

  17. Some scholars request introducing mandatory algorithmic risk and human rights impact assessments for any AI-based application, not just for high-risk systems, as is currently planned. The risk levels could then be decided on a case-by-case basis, and systems either be banned or classified as high-risk. Such assessments would also promote the auditability and explainability of AI systems [94, 106]).

  18. A related concept—unlawfulness by default [122]—requires changing the burden of proof: i.e., the default is unlawfulness or unethicality, and AI providers have the burden to demonstrate that AI systems are not causing any harm, such as unfair/discriminatory decisions or inaccurate results, before they are marketed.

  19. Critics recommend making the standardization and risk-classifying process more transparent and inclusive, i.e., to have a better representation of stakeholder interests and counterbalance the adverse effects of private rulemaking (and the corresponding power imbalances between AI providers and other [civil society] stakeholders) [82]. It would require, among others, substantive information rights for affected individuals, adding public participation rights for citizens, e.g., regarding the decision to amend the list of high-risk systems, and ensuring that not only corporate and expert groups are involved in the standardization and risk-classifying process by actively involving organizations which represent public interests [101].

  20. This is especially important in the context of (real-time) workplace monitoring, the potentially negative impacts of ‘smart manufacturing’ on labor markets (i.e., possible job losses), and other areas that might affect workers’ rights (including freedom/autonomy rights).

  21. E.g., it is crucial to provide some form of (minimum) harmonized implementation guidance to member states to prevent uneven or unreliable enforcement at the national level [101].

  22. Moreover, E.U. policy and lawmakers should work towards harmonizing international AI standards and guidelines. Of particular importance in this regard is the transatlantic cooperation. Unified—and ideally global—standards for AI technologies would prevent regulatory gaps and ‘forum shopping’ (i.e., companies moving to countries with less regulatory burden and compliance costs) and would help in creating a level playing field with a minimum degree of legal certainty and planning security, as envisioned by ordoliberalism [45, 119].

  23. AlgorithmWatch [94] and others demand the explicit ban of all biometric mass surveillance technologies. According to the organization, the term ‘real-time remote biometric identification systems’ includes too many exceptions and ethical issues. For instance, it enables indiscriminate (i.e., arbitrarily or discriminatorily targeted) mass surveillance, which is incompatible with fundamental (human) rights and undermines key principles of rule-of-law societies. The organization also urges lawmakers to ensure that the ban applies to all public authorities, private actors that act on behalf of public authorities, and to post biometric identification systems—not only real-time. Lastly, the organization demands closing (some of) the AIA’s loopholes, e.g., by removing the exceptions in Art. 5 [94]. Other researchers recommend expanding the scope of the prohibition on social scoring to private actors, extending the ban on remote biometric identification systems in public spaces to non-law enforcement public actors, prohibiting the use of remote live biometric categorization systems in public places and the use of emotion recognition systems, and adding biometric categorization systems and emotion recognition systems to the list of high-risk systems [101] [note that some of those concerns have been addressed by the Council and Parliament (see Sect. 3)]. Most importantly, the Commission should be enabled to add AI technologies to the list of prohibited practices or high-risk systems. Here, it is crucial that the process of banning or adding high-risk categories is done in an inclusive, transparent, and democratic manner, that is, through robust consultation and stakeholder engagement, and that civil society representatives are heard. Also, all systems should be subject to prior independent conformity assessment control [101].

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The author would like to thank two anonymous reviewers for their invaluable constructive feedback and criticism. They helped to improve the article significantly. The usual caveats apply.

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    Manuel Wörsdörfer

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Wörsdörfer, M. The E.U.’s artificial intelligence act: an ordoliberal assessment. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00337-x

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