Beyond the AI Act: Governing Europe’s Tech Future

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Author: Denis Avetisyan

New research reveals that effective AI governance hinges less on crafting adaptable laws and more on securing the resources needed to actually enforce them.

A Policy Delphi study demonstrates a critical gap between ambitious AI governance goals and the practical realities of implementation and enforcement in Europe.

While ambitious visions for adaptable AI regulation abound, practical implementation often lags behind technological advancement. This tension is explored in ‘Governing rapid technological change: Policy Delphi on the future of European AI governance’, a study employing the Delphi method to gauge expert perspectives on the future of European AI governance. Our analysis reveals that a future-proof approach hinges less on the specifics of legislation and more on committed resources for its enforcement, highlighting a significant gap between desirable policy goals – such as increased citizen participation – and their perceived feasibility. Can anticipatory governance frameworks effectively bridge this divide and ensure responsible innovation in the rapidly evolving landscape of artificial intelligence?

The Inevitable Calculus of AI Governance

The accelerating pace of artificial intelligence development promises transformative benefits across numerous sectors, from healthcare and education to environmental sustainability and economic productivity. However, this rapid innovation is not without inherent risks; potential harms range from algorithmic bias and job displacement to the erosion of privacy and the emergence of autonomous weapons systems. Consequently, effective governance of AI is no longer simply desirable, but essential. This necessitates a shift from reactive regulation – addressing problems after they arise – to a proactive approach that anticipates future challenges and shapes the trajectory of AI development. Forward-thinking governance involves establishing ethical guidelines, promoting transparency and accountability in AI systems, and fostering international collaboration to mitigate global risks, ultimately ensuring that the benefits of AI are widely shared while minimizing potential harms.

Historically, regulation has largely functioned as a response to established harms, a pattern proving increasingly inadequate in the face of rapidly evolving technologies. Current regulatory frameworks, designed for slower-paced innovation, often struggle to anticipate the complex implications of artificial intelligence before those impacts are realized – leading to a constant cycle of catch-up. This reactive approach not only hinders the ability to mitigate potential risks, such as algorithmic bias or job displacement, but also stifles beneficial innovation by imposing constraints after development has occurred. The inherent lag between technological advancement and regulatory response creates a persistent gap, demanding a shift towards more anticipatory and adaptable governance strategies to effectively navigate the challenges and harness the opportunities presented by AI.

A truly effective approach to artificial intelligence governance necessitates a shift from responding to problems as they arise to actively envisioning and preparing for future challenges. This proactive stance demands that policymakers and developers collaborate to anticipate potential societal impacts – from workforce displacement and algorithmic bias to issues of data privacy and autonomous weapons systems. By strategically shaping the development of AI – through incentivizing responsible innovation, establishing clear ethical guidelines, and fostering ongoing research into safety and security – governance can steer the technology towards outcomes that benefit all of humanity. Such foresight isn’t about hindering progress, but rather about ensuring that innovation unfolds within a framework that maximizes opportunities while minimizing risks, ultimately fostering a future where AI serves as a powerful force for good.

A lack of proactive governance in the realm of artificial intelligence risks creating a future where innovation is paradoxically hampered by avoidable constraints. When regulatory frameworks lag behind technological progress, developers may self-censor, prioritizing legal safety over potentially groundbreaking advancements. More critically, the absence of foresight allows for the unchecked development of systems that could exacerbate existing societal inequalities or introduce new forms of harm – from biased algorithms perpetuating discrimination to autonomous systems lacking ethical considerations. This isn’t simply a matter of slowing progress; it’s about ensuring that the benefits of AI are broadly shared and that its risks are mitigated before they manifest as tangible societal problems, necessitating reactive measures that are often less effective and more costly than preventative ones.

Anticipating the Future: Methodologies for Proactive Policy

The Policy Delphi method is an iterative forecasting process that solicits opinions from a panel of experts to achieve a reliable consensus on complex policy questions. Unlike traditional expert panels or surveys, Delphi employs a structured, anonymous process involving multiple rounds of questionnaires. Each round presents experts with the aggregated responses from the previous round, allowing them to revise their own assessments in light of the collective judgment. This iterative feedback loop continues until a stable distribution of opinions emerges, indicating a degree of convergence and identifying areas of substantial agreement or persistent disagreement. The method is particularly useful for addressing AI policy issues characterized by high uncertainty and a lack of empirical data, as it systematically distills expert knowledge and identifies potential future trends.

Regulatory sandboxes facilitate the controlled deployment of novel AI technologies in a live environment with a limited scope and duration. These sandboxes typically involve a collaborative effort between innovators, regulators, and relevant stakeholders to test and evaluate AI systems before widespread implementation. Key features include relaxed regulatory requirements within the sandbox, defined boundaries to contain potential harms, and robust monitoring and evaluation mechanisms to assess performance, identify risks, and inform policy development. The objective is to gather empirical data on the real-world impacts of AI, allowing regulators to refine policies based on observed outcomes and fostering innovation while mitigating potential negative consequences. This approach reduces risks associated with untested technologies and enables a more informed and adaptive regulatory framework.

Anticipatory Governance is supported through methodologies that facilitate the systematic consideration of future possibilities and the preemptive development of regulatory frameworks. This involves stakeholders collaboratively identifying potential future states influenced by AI technologies, assessing associated risks and opportunities, and formulating policy options before those scenarios materialize. By proactively addressing potential challenges and fostering innovation within defined boundaries, anticipatory approaches aim to minimize negative consequences and maximize societal benefits. This contrasts with reactive policymaking, which addresses issues only after they have become pressing concerns, potentially limiting the range of effective responses and increasing associated costs.

Combining expert elicitation techniques, such as the Policy Delphi method, with controlled experimentation via regulatory sandboxes provides a cyclical and robust framework for informed policymaking. Expert elicitation identifies potential future issues and a range of possible responses, while regulatory sandboxes allow for the testing of these responses in a limited, real-world environment. Data gathered from these experiments then informs and refines the initial expert assessments, creating an iterative process. This combination minimizes risks associated with novel AI technologies by providing empirical data to complement expert opinion, and facilitates the development of more effective and adaptable regulations grounded in both foresight and practical evidence.

Constraints on Equitable AI: A Systemic Analysis

The AI industry is experiencing a rapid concentration of power among a limited number of large technology companies. This consolidation is evidenced by the substantial capital requirements for AI development, particularly for training large language models, which creates high barriers to entry for smaller organizations and startups. Control over critical resources – including data, computing infrastructure, and specialized talent – is increasingly held by these dominant players. This concentration poses a risk to equitable AI governance as it limits diverse perspectives in the development and deployment of AI systems, potentially reinforcing existing societal biases and hindering the creation of AI that benefits all stakeholders. Furthermore, a small number of entities controlling key AI technologies may be less responsive to public concerns regarding fairness, transparency, and accountability.

Industrial policy, while intended to foster growth in the artificial intelligence sector, can unintentionally strengthen market concentration. Direct subsidies, tax breaks, and preferential treatment for large AI firms can create barriers to entry for smaller companies and startups, hindering competition. This effect is amplified by economies of scale inherent in AI development-particularly the high costs associated with data acquisition, computational resources, and specialized talent. Consequently, industrial policies may lead to the consolidation of power within a few dominant players, reducing innovation as these firms face diminished incentives to invest in novel approaches and potentially stifling the development of a diverse AI ecosystem. This dynamic risks creating monopolies or oligopolies that prioritize profit maximization over broader societal benefits.

The Digital Omnibus, encompassing regulations such as the AI Act, and related initiatives designed to govern AI systems, currently face substantial challenges regarding enforcement capacity. These difficulties stem from a combination of factors including limited resources allocated to regulatory bodies, a scarcity of qualified personnel possessing the technical expertise to assess complex AI systems, and the rapid pace of AI development which often outstrips the ability of regulators to keep up. Specifically, verifying compliance with requirements regarding data governance, algorithmic transparency, and risk mitigation necessitates extensive auditing and ongoing monitoring, creating a significant logistical burden. Furthermore, the global nature of AI development and deployment complicates enforcement, requiring international cooperation and coordination, which remains underdeveloped. This lack of robust enforcement capacity threatens to undermine the effectiveness of these regulatory frameworks and limit their ability to ensure responsible AI innovation.

Analysis of expert opinions reveals a substantial discrepancy between the perceived desirability and practical probability of certain AI governance approaches. While a strong consensus exists among specialists regarding the benefits of inclusive mechanisms such as citizen participation and multilateral international governance frameworks, these are consistently rated as less feasible for near-term implementation compared to adjustments and enforcement of currently available regulatory tools. This “desirability-probability gap” suggests a prioritization of pragmatic, incremental regulatory steps over more ambitious, structurally transformative governance models, despite acknowledging the potential long-term advantages of the latter.

Towards a Stable Equilibrium: Charting a Course for Responsible AI

The increasing centralization of artificial intelligence development within a small number of powerful entities presents significant risks, but investment in digital public infrastructure offers a viable countermeasure. This approach advocates for publicly funded and maintained platforms – encompassing data repositories, computational resources, and algorithmic tools – that are accessible to a wide range of users, including researchers, startups, and civil society organizations. By providing these alternatives, dependence on proprietary systems diminishes, fostering competition and innovation beyond the control of a few dominant players. Such infrastructure not only lowers the barriers to entry for AI development but also promotes transparency and accountability, enabling independent audits and preventing the entrenchment of biased or harmful algorithms. Ultimately, a robust digital public infrastructure serves as a crucial component in democratizing access to AI and ensuring its benefits are broadly shared, rather than concentrated within a limited scope of power.

A pragmatic approach to artificial intelligence governance necessitates prioritizing risks, as exemplified by the European Union’s AI Act. This legislation moves beyond a blanket regulatory structure, instead categorizing AI systems based on their potential to cause harm – ranging from minimal risk to unacceptable risk. This tiered system allows regulatory bodies to concentrate resources and expertise on applications posing the greatest threat to fundamental rights, safety, and democratic processes. By focusing on high-risk AI – such as systems used in critical infrastructure, education, or law enforcement – regulators can implement stringent requirements regarding transparency, data governance, and human oversight. This targeted strategy acknowledges the limitations of oversight capacity and ensures that interventions are proportionate to the level of risk, ultimately fostering innovation while safeguarding against potential harms. The EU AI Act, therefore, serves as a model for effective AI governance by prioritizing a risk-based methodology for resource allocation and regulatory enforcement.

The effective implementation of artificial intelligence hinges on bridging the gap between what is considered desirable in AI development and the actual probability of achieving safe and beneficial outcomes. Current research indicates a significant disconnect, with experts consistently prioritizing the allocation of sufficient resources to regulatory authorities for robust enforcement as the most crucial step. This emphasis stems from a recognition that even the most elegantly designed ethical guidelines or technical safeguards are ineffective without the means to ensure compliance. Successfully navigating this “desirability-probability gap” necessitates sustained and open dialogue not only amongst AI specialists, but also between policymakers tasked with crafting appropriate legislation and the broader public whose values and concerns should inform the development process. Such collaboration is vital to ensure that AI innovation aligns with societal expectations and that potential risks are proactively addressed through effective oversight and accountability.

The future of artificial intelligence hinges not merely on technological advancement, but on a deliberate and forward-thinking approach to its governance. A synergistic combination of proactive policies, widespread access to digital infrastructure, and carefully considered regulation offers a pathway to harness AI’s transformative potential while actively minimizing its inherent risks. This isn’t simply about controlling the technology, but about shaping its development and deployment to ensure equitable benefits and widespread resilience. Equitable infrastructure, ensuring broad participation and preventing monopolization, is foundational, while effective regulation, grounded in risk assessment and adaptable to rapid innovation, provides the necessary guardrails. Ultimately, this combined strategy allows for a future where AI serves as a catalyst for progress, rather than a source of concentrated power or unforeseen harm, unlocking benefits for all segments of society and fostering long-term stability.

The study underscores a critical point regarding AI governance: the chasm between aspirational policy and practical realization. It’s not enough to simply define ‘safe AI’ or establish broad principles; the true measure lies in committed resources for implementation and enforcement. This echoes Edsger W. Dijkstra’s sentiment: “It’s not enough to try to be right, you have to succeed in being right.” The research highlights that even with the forthcoming AI Act, the success of European AI governance hinges not on the flexibility of the regulation, but on the demonstrable ability to apply and enforce it, thereby translating intention into verifiable outcome. A mathematically pure definition of risk is useless without the tools to rigorously assess and mitigate it.

The Road Ahead

The presented work illuminates a persistent incongruity within the discourse of technological governance: the seductive allure of elegant principles versus the messy reality of their enactment. To propose ‘future-proof’ regulation is, in a sense, a logical fallacy. The future, by definition, resists precise formulation. The study suggests that a surfeit of adaptable, ‘technology-neutral’ approaches, while intellectually appealing, may simply defer the essential, and often unglamorous, work of resource allocation and diligent enforcement. The AI Act, and similar legislative efforts, are not self-executing theorems; they require a commitment to practical application.

Future research must therefore move beyond the refinement of abstract regulatory frameworks. A fruitful avenue lies in comparative analysis – not of legal texts, but of actual implementation strategies. Which nations, and which agencies, are demonstrably equipped to assess risk, monitor compliance, and meaningfully address harms arising from AI systems? The focus should be on the operational mechanics of governance, not merely its aspirational pronouncements.

Ultimately, the question is not whether a regulation is ‘flexible’ enough to accommodate future innovation, but whether it is grounded in a clear understanding of present capabilities and a realistic assessment of the political will required to enforce it. The pursuit of perfect foresight is a distraction. A pragmatic, empirically-driven approach – one that prioritizes demonstrable action over theoretical elegance – offers a more promising path forward.

Original article: https://arxiv.org/pdf/2512.15196.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-18 23:28