AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This paper advances a layered ethics-stack perspective and argues that AI governance should be evaluated not only by the presence of principles, but also by the distribution of operational controls, positive commitments, and enforceable constraints across institutional layers.
Large language models have become central infrastructures of contemporary digital economies while raising persistent ethical concerns regarding linguistic inequality, opacity, data governance, and the concentration of technological power. Much of the current debate on AI ethics focuses on normative principles such as fairness, transparency, and accountability. While these principles remain essential, they often do not sufficiently explain why ethically problematic outcomes persist under competitive market conditions. This paper addresses that gap by applying Karl Homann’s institutional economic ethics to the governance of large language models. From this perspective, ethical deficits in AI development are not merely the result of individual failures of responsibility, but are also shaped by institutional incentive structures that reward speed, scale, proprietary control, and strategic secrecy. The paper analyses three central ethical challenges in LLM development: linguistic and cultural asymmetries, transparency and accountability deficits, and contested practices of data governance and intellectual property. It then argues that the familiar opposition between open and closed AI systems is conceptually and institutionally inadequate. In response, the paper develops the concept of partially open AI governance, understood as a differentiated arrangement of access, disclosure, and oversight across distinct layers of AI systems. Such an approach offers a more realistic way of aligning innovation incentives with ethical and public objectives in the governance of large language models.
The rapid global deployment of artificial intelligence (AI) has created governance challenges relating to accountability, privacy, corporate oversight, and regulatory compliance. While the European Union (EU) Artificial Intelligence Act (AI Act) and the General Data Protection Regulation (GDPR) are often analysed separately, limited scholarship has examined how both frameworks operate together in shaping corporate governance beyond the EU. This article investigates how the AI Act and the GDPR function as complementary regulatory instruments that reconfigure governance structures within firms and influence corporate decision-making globally. The research adopts a doctrinal and comparative legal methodology, informed by regulatory governance theory, combining analysis of EU legislative texts, enforcement practice, academic literature, and governance guidance. Building on work in digital regulation and regulatory capitalism, the study finds that both instruments extend governance-by-design principles into corporate practice by embedding compliance obligations into risk management systems, internal controls, board oversight, and accountability processes (Bradford, 2020; De Gregorio & Dunn, 2022). The article concludes that the AI Act and GDPR operate as twin pillars of digital governance that enable the EU to govern corporate conduct at a distance. The paper contributes to scholarship on AI regulation and corporate governance by offering an original analytical framework relevant to both academics and practitioners.
Artificial intelligence is increasingly used in public administration to classify individuals, assess risks, prioritize cases, support eligibility determinations and guide the allocation of public resources. In the European Union, these uses are governed by the Artificial Intelligence Act, the GDPR and the Charter of Fundamental Rights. Formal compliance, however, does not by itself ensure lawful and accountable administration. AI relocates discretion from the visible act of decision-making to less visible choices concerning data, model design, procurement, thresholds and interface architecture. This Policy and Practice Review therefore treats human-centric AI governance not as a general ethical aspiration, but as an administrative and constitutional framework for governing public power. Drawing on EU law, public administration scholarship and a comparative institutional analysis of selected Member State practices, it develops six interdependent dimensions: legal anchoring, accountable discretion, fundamental rights by design, meaningful human oversight, contestability and justification, and institutional resilience. The analysis shows that common EU rules may produce unequal levels of protection where public authorities differ in technical expertise, audit capacity, procurement independence and access to effective remedies. It also argues that accountability must follow the chain of influence through which algorithmic systems shape administrative outcomes, rather than only the formal chain of decision-making. The article translates this framework into actor-specific recommendations concerning fundamental rights impact assessments, procurement, auditability, human oversight, transparency, contestability and post-deployment monitoring. It concludes that AI-enabled administration remains legitimate only where public authorities retain the capacity to understand, justify, correct, suspend and democratically control the systems they use.
. AI ethics has recently emerged as a dominant governance paradigm, increasingly implemented through institutionalised, specialised and expert-driven tools and mechanisms, thereby compelling us to revisit the question of democratic deficit . This ethics-oriented technocratic approach is exemplified by the EU AI Act (2024), which promotes soft-law tools as part of a layered, risk-based governance framework. This article critically examines whether that model can address the democratic deficit in AI governance, focusing on harmonised standards, voluntary codes of conduct and regulatory sandboxes . Drawing on democratic theory and governance scholarship, it explores the extent to which these instruments advance participation, deliberation and accountability. By assessing these mechanisms as key sites of governance, the article concludes that they prioritise flexibility, technical expertise and market integration over public contestation and participation, offering little response to the democratic deficit. It argues that AI ethics can address this deficit only when complemented by institutional reforms that embed deliberation, broaden participation and ensure meaningful public accountability, including the involvement of non-expert citizens.
The central claim is that constitutional and democratic requirements should not be treated as external compliance burdens when embedded into institutional design, they operate as productive constraints that improve legitimacy, implementation discipline, and the long-term trustworthiness of AI-enabled public decision-making.
Artificial Intelligence (AI) has become a central focus of ethical deliberation in reports, guidelines, and official statements issued by technology companies, governments, and international organizations. This paper analyzes five influential documents representing the broad field of AI ethics as a ‘sociotechnical imaginary’: a collectively held, institutionalized, and publicly performed vision of the future. It argues that this imaginary transforms ethics into a technology of governance through which governments, international organizations, and corporations present AI as an inevitable, controllable, and desirable future. The paper critically examines the limitations of this approach, particularly its claims to universality, which treat both AI and ethics as abstract, cultureless objects detached from time and place. Drawing on Science and Technology Studies (STS) and philosophy, the paper outlines alternative pathways toward historically, geographically, and politically situated forms of ethics, in which technological futures remain collective, contested, and open rather than confined to trajectories presented as inevitable.
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