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AI ethics and ordoliberalism 2.0: towards a ‘Digital Bill of Rights’

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Abstract

This article analyzes AI ethics from a distinct business ethics perspective, i.e., ‘ordoliberalism 2.0.’ It argues that the ongoing discourse on (generative) AI relies too much on corporate self-regulation and voluntary codes of conduct and thus lacks adequate governance mechanisms. To address these issues, the paper suggests not only introducing hard-law legislation with a more effective oversight structure but also merging already existing AI guidelines with an ordoliberal-inspired regulatory and competition policy. However, this link between AI ethics, regulation, and antitrust is not yet adequately discussed in the academic literature and beyond. The paper thus closes a significant gap in the academic literature and adds to the predominantly legal-political and philosophical discourse on AI governance. The paper’s research questions and goals are twofold: first, it identifies ordoliberal-inspired AI ethics principles that could serve as the foundation for a ‘digital bill of rights.’ Second, it shows how those principles could be implemented at the macro level with the help of ordoliberal competition and regulatory policy.

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Notes

  1. Other notable examples include the Future of Life Institute’s ‘Asilomar AI Principles’ (2017), UNI Global Union’s ‘Top 10 Principles for Ethical AI’ (2017), Council of Europe’s ‘European Ethical Charter on the Use of AI in Judicial Systems’ (2018), European Commission’s ‘AI for Europe’ (2018), Germany’s ‘AI Strategy’ (2018), ‘Beijing AI Principles’ (2019), G20’s ‘AI Principles’ (2019), High-Level Expert Group on AI’s ‘Ethics Guidelines for Trustworthy AI’ and ‘Policy and Investment Recommendations’ (2019), IEEE Global Initiative’s ‘Ethically Aligned Design’ (2019), OECD’s ‘Principles on AI’ (2019), Global Partnership on AI (2020), E.U.’s ‘White Paper on AI’ (2020), U.K.’s National AI Strategy (2021), E.U.’s ‘Proposal for a Regulation on a European Approach to Artificial Intelligence’ (2021), and Khanna’s ‘Internet Bill of Rights’ (2022). Furthermore, several (inter-)national standardization efforts are underway, e.g., by standard-developing organizations such as ISO, IEC, NIST, CEN, and CENELEC [3, 73, 79, 120].

  2. I.e., respect for human rights, data protection and the right to privacy, harm prevention and beneficence, non-discrimination and freedom of privileges, fairness and justice, transparency and explainability of AI systems, accountability and responsibility, democracy and the rule of law, and environmental and social sustainability.

  3. O’Neil [102] classifies AI systems as ‘weapons of math destruction’ since they negatively impact the marginalized and vulnerable parts of society, i.e., low-income people and ethnic minorities. That is, they often lead to more discrimination, racism, and prejudices, e.g., due to biased software and data, thereby increasing inequality, deepening the social divide, and negatively impacting democracy and the rule of law. AI systems also reinforce negative feedback loops and vicious circles, e.g., in the form of poverty traps. Lastly, victims of AI discrimination and racism have (almost) no means to file a complaint and mitigate their harms and adverse impacts [27, 78].

  4. The main reasons for algorithmic biases include the poor selection of training data, especially unrepresentative or incomplete data sets (e.g., relying only on white male U.S. population data or having other cultural or ethnic biases), predictions based on too little data—and thus the impossibility of generalizations –, flawed correlations or not considering the underlying causations, and a lack of diversity among AI developers and data scientists. Note that most computer science teams are dominated by white male Westerners aged 20-40—so-called ‘male AI’; not adequately represented, however, are BIPOC, women, disabled and elderly people, and people from developing countries. Also, note that AI technologies are social artifacts that embed and project AI developers’ choices, biases, and values; that is, personal beliefs, opinions, and prejudices, as well as stereotypes and societal biases of computer scientists play a significant role and are reflected in algorithms (i.e., ‘bias in, bias out’) [18, 21, 24, 27, 86, 115].

  5. Net neutrality is thus also crucial to realize the ordoliberal concept of ‘justice of the starting conditions’ [141], discussed in the next section.

  6. Ordoliberals consider science and academics (i.e., ‘clercs’) as a potential ordering power in society [42, 112]. In the context of AI, academic researchers play an essential role in spreading digital literacy and user awareness, besides, they bear special responsibilities regarding addressing and mitigating the societal impacts of AI technologies and promoting the common good.

  7. According to Fjeld et al. [61], professional responsibility includes accuracy, responsible design, consideration of long-term effects, multi-stakeholder collaboration, and scientific integrity (i.e., following corporate or professional codes of ethics and standards, such as a Hippocratic oath for data scientists and computer professionals).

  8. I.e., private property is linked to and has to serve the public good [42], see for the social commitments of property owners: [11, 43] (here, Eucken defines private property as an essential instrument in both economic and social terms and stresses the socio-economic and political responsibilities of entrepreneurs).

  9. Novelli et al. [101] distinguish between various accountability conditions (authority recognition, interrogation, and limitation of power), features (context, range, agent, forum, standard, process, and implications), and goals (compliance, report, oversight, and enforcement), as well as between proactive (‘accountability as virtue’ with the intent to prevent failures) and reactive accountability (‘accountability as a mechanism’ to redress failures).

  10. Other points of criticism include the AIA’s tendency to prioritize economic, business, and innovation over moral concerns (i.e., the de-prioritization of human rights), the lack of a clear definition of AI systems (i.e., a lack of scope), the flawed risk-based framework (i.e., an incomplete list of prohibited AI systems and under-regulation of non-high-risk AI systems), and the failure to adequately address the challenges posed by generative AI, such as chatbots and deepfakes [59, 63, 65].

  11. Note that Eucken and other ordoliberals saw unions as an essential counterweight to the power of employers [35, 36, 42].

  12. Experts recommend making the risk classification and standardization process more inclusive and transparent [31]. This would require substantive information rights for affected individuals, adding public participation rights for citizens, and ensuring that not only corporate and expert groups are involved in the classification and standardization process by actively involving organizations that represent public interests [121]. It might also be worth exploring whether the EAIB’s responsibilities could be expanded. Currently, the board serves in an advisory role, and critics claim that its tasks should be amended to include investigatory and regulatory powers. Furthermore, it could be transformed into a stakeholder forum to overcome the previously mentioned issues of lack of consultation, participation, and stakeholder dialogue [22, 23]. Besides reforming the AIA, democratic accountability and judicial oversight require an ordoliberal-inspired competition policy and a strengthening of the DMA (see below).

  13. Note that an ordoliberal-inspired social and environmental market economy would mandate the internalization of adverse external effects (e.g., via carbon pricing or emissions trading schemes) and would thus help reduce energy usage—including the ones of AI systems—and promote green environments.

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Wörsdörfer, M. AI ethics and ordoliberalism 2.0: towards a ‘Digital Bill of Rights’. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00367-5

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