In an era where artificial intelligence (AI) can compose symphonies, diagnose illnesses, and generate intricate artworks, the very fabric of expertise is being fundamentally rewoven. The “instant expertise paradox” emerges as AI grants individuals near-immediate access to expert-level outputs without the foundational experiences traditionally required to attain such proficiency (Grace et al. 2018). As AI systems democratize knowledge creation and dissemination, society faces a profound dilemma: when everyone appears to be an expert, who can be trusted?

The relationship between expertise and trust is fundamental to professional knowledge systems: trust emerges from the demonstrated ability to apply expertise within appropriate contexts. When AI systems generate expert-level outputs without this contextual foundation, they risk undermining both the trust in their own outputs and the broader professional knowledge frameworks they operate within.

This risk becomes particularly acute because, unlike previous technological revolutions, AI does not merely provide access to information; it synthesizes and presents it in ways that emulate human thought and creativity (Miller 2019). This creates an illusion of expertise that often lacks the depth and context of genuine understanding. This transformation is particularly evident in professional domains where AI systems can now perform complex analytical tasks that previously required years of specialized training. For instance, in medical imaging, AI systems can identify patterns with accuracy rivaling human experts, yet they lack the holistic understanding that comes from clinical experience. This highlights the instant expertise paradox: while AI can replicate expert-level outputs, it may miss crucial contextual factors that human experts naturally consider in their decision-making process.

This fundamental limitation extends beyond individual tasks to broader domains of expertise—whether in healthcare, law, or other professional fields (Amann et al. 2020). The synthesis that AI performs lacks the empathy, ethical considerations, and contextual awareness that come from human experience. For instance, while an AI may identify a pattern in a medical scan, it cannot understand the patient’s fears or weigh the cultural and personal factors that influence treatment choices. This highlights a crucial limitation: AI can provide outputs that seem “expert,” but lacking the depth of lived experience and empathy, these outputs can become dangerously superficial, lacking the holistic approach required in real decision-making.

The transformation of expertise extends beyond individual capabilities and fundamentally reshapes entire knowledge networks. AI algorithms now curate content, moderate discussions, and generate responses across professional forums, social media platforms, and collaborative communities (Tessler et al. 2024). While this automation enhances efficiency and broadens participation, it risks homogenizing perspectives by prioritizing mainstream views at the expense of minority opinions. Recent studies demonstrate that such algorithmic curation can foster echo chambers that stifle innovation and reduce cognitive diversity (Doshi and Hauser 2024; Kusters et al. 2020; Sasahara et al. 2021).

Traditional expert hierarchies, built upon years of study, practice, and peer recognition, are also being disrupted. AI tools increasingly empower individuals to perform tasks that previously required specialized training, ranging from medical image interpretation to legal analysis. While democratizing expertise offers many benefits, it raises serious concerns about the reliability of AI-assisted judgments and the erosion of professional standards. Additionally, the temporal dynamics of expertise are shifting, with certain skills becoming obsolete more rapidly due to AI advancements, particularly in fields relying on routine analysis or pattern recognition.

These challenges to expertise and trust systems necessitate regulatory responses, as they affect the fundamental mechanisms through which professional knowledge is validated and applied.

Current regulatory approaches and their limitations

The European Union’s AI ActFootnote 1 represents the most comprehensive attempt to regulate AI systems to date, introducing mandatory requirements for transparency, traceability, and human oversight. Recent scholarship (Park 2024) has highlighted how this approach exemplifies a “horizontal” regulatory framework that “postulates the homogeneity of AI systems,” in contrast to the context-specific approaches adopted by other jurisdictions. While the Act establishes a robust framework for AI governance, a detailed analysis of its provisions reveals important gaps in addressing expertise contextualization. Articles 13 and 14 focus primarily on technical transparency and operational oversight, respectively, but do not explicitly address how AI-generated expertise should be situated within professional knowledge frameworks. Article 13’s transparency requirements emphasize system status disclosure without considering the broader context of professional expertise, while Article 14’s human oversight provisions concentrate on operational monitoring rather than expertise validation.

While organizational practices play a crucial role in AI implementation, regulatory frameworks can establish necessary guardrails for contextualizing AI-generated expertise. Rather than prescribing specific organizational approaches, Expertise Contextualization creates a structured framework within which organizations can develop appropriate implementation strategies.

These provisions aim to ensure that AI systems remain accountable and their outputs verifiable. However, research indicates that users tend to over-trust AI systems, particularly when they present information in authoritative ways (Klingbeil et al. 2024; Schmidt et al. 2020). The Act’s requirements, while valuable, do not explicitly address how AI-generated expertise should be contextualized within broader knowledge frameworks. This regulatory gap becomes particularly critical as AI reshapes professional domains. While AI can match or exceed human performance in specific tasks, it often fails to capture the nuanced understanding that emerges from professional experience. Recent work highlights how large language models (LLMs), despite their impressive ability to generate sophisticated responses, remain fragile and highly sensitive to input variations (Huang et al. 2024). Even minor changes in problem framing can result in significantly different outputs, pointing to an underlying lack of true comprehension (Mirzadeh et al. 2024). The EU AI Act’s focus on technical oversight mechanisms, though necessary, may not fully address the challenges posed by the instant expertise paradox and the transformation of knowledge networks.

Beyond traditional safeguards: introducing expertise contextualization

To address these limitations, it is proposed to extend the EU AI Act’s principles by adding a fourth regulatory pillar: Expertise Contextualization. This aligns with emerging research calling for “contextual, coherent, and commensurable” regulatory frameworks that can better address the nuanced challenges posed by different AI applications while facilitating international harmonization (Park 2024). This requires AI systems to actively communicate the boundaries and context of their expertise, going beyond mere transparency to ensure that AI-generated outputs are properly situated within the broader landscape of human knowledge and real-world complexity.

While organizational practices play a crucial role in expertise integration, regulatory frameworks provide the necessary foundation for consistent implementation across different contexts. As a regulatory pillar, Expertise Contextualization mandates specific requirements for context communication and boundary definition, similar to how existing pillars mandate requirements for transparency and oversight. This regulatory approach ensures that organizations have the necessary tools and standards for effective expertise validation while maintaining flexibility in implementation approaches.

The concept of Expertise Contextualization builds upon established governance theories, particularly polycentric governance frameworks (Ostrom 2010), which emphasize the importance of multiple, interconnected decision-making centers. Just as polycentric systems enable adaptive governance through distributed authority and local knowledge integration, Expertise Contextualization creates multiple points of validation and interpretation for AI-generated expertise. This approach aligns with reflexive regulation theory’s emphasis on self-regulatory mechanisms that respond to complex social systems (Black 2001; Teubner 1983). By embedding context markers within AI systems, the framework enables dynamic self-adjustment based on operational boundaries and real-world complexity.

Table 1 provides a comparative overview of how Expertise Contextualization enhances existing framework components to overcome current limitations and address the need for robust, trustworthy AI integration.

Table 1 Integration of Expertise Contextualization with the EU AI Governance Framework.
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Expertise Contextualization is designed to ensure that AI-generated information is critically evaluated with an understanding of the limitations and scope of AI’s capabilities. Drawing from established frameworks in professional knowledge management (Eraut 2000) and AI governance (Floridi et al. 2018), as well as recent research on human-AI collaboration, Expertise Contextualization operates through four key mechanisms:

  1. (1)

    Dynamic knowledge boundary maps: these maps explicitly identify areas where AI-generated expertise ends and where human interpretation becomes essential. By clearly demarcating the limits of AI, users are better equipped to understand when they need to consult human experts.

  2. (2)

    Contextualized confidence metrics: traditional AI confidence scores are often presented as percentages that indicate the system’s certainty. However, these metrics lack nuance regarding the complexity of real-world situations. Contextualized confidence metrics consider not only statistical certainty but also the broader implications of AI outputs, including ethical considerations and the degree of human intervention required. This aligns with recent research on explainable AI and the need for interdisciplinary expert review of AI model explanations (Bennett et al. 2024).

  3. (3)

    Domain-specific expertise frameworks: these frameworks help users understand how AI-generated outputs relate to established professional knowledge and practices. By situating AI outputs within domain-specific contexts, users can better judge the reliability of the information. For instance, a diagnostic suggestion from an AI in healthcare would be linked to established medical protocols and standards, indicating where human expertise is indispensable.

  4. (4)

    Temporal context indicators: expertise is not static, and the value of AI-generated insights may change over time as new information becomes available. Temporal context indicators acknowledge the rapidly evolving nature of expertise, signaling to users when AI outputs are potentially outdated or require further human validation. This is particularly relevant in fields with rapidly evolving knowledge bases, such as healthcare and scientific research.

By incorporating these mechanisms, Expertise Contextualization provides a structured way to bridge the gap between AI outputs and human expertise, thereby reducing over-reliance on AI and ensuring that machine-generated insights are treated as complementary rather than definitive.

Implementing expertise contextualization: practical measures

The implementation of Expertise Contextualization addresses three key challenges identified in AI governance literature: validation of AI-generated expertise, integration with professional practice, and maintenance of accountability. The following measures provide practical pathways toward addressing these challenges while ensuring robust integration into existing frameworks:

Regulatory pilot programs

Derived from established practices in regulatory sandboxes and pilot testing of emerging technologies (Saurwein et al. 2015), these programs serve as “safe” environments where novel governance measures—such as Expertise Contextualization—can be trialed, evaluated, and refined. These pilot programs will provide empirical evidence on the benefits and challenges of integrating context-aware features into AI systems, thereby refining regulatory guidelines based on real-world data.

Cross-industry standards

Research on standardization in emerging technologies suggests that unified guidelines reduce fragmentation and enhance interoperability (Blind 2016). In AI governance, consistent reference frameworks for confidence metrics, domain boundaries, and temporal context markers can prevent conflicting regulations between sectors and ensure a baseline of “contextualized” AI outputs. These standards address the challenge of integrating AI with professional practice: by aligning multiple industries under common protocols, AI developers and end-users alike gain a clear, consistent approach to interpreting machine-generated expertise.

Expertise repositories

They should be incentivized to enhance validation and accountability by government and industry bodies through grants and other resources. In knowledge management literature (Davenport and Prusak 1998), well-curated knowledge bases are widely recognized as critical references for preserving institutional expertise and ensuring alignment with established professional standards, thus preventing AI solutions from straying into unreliable domains. These repositories will serve as foundational references for AI systems, ensuring that outputs are aligned with professional knowledge. By providing the context needed for proper interpretation, these repositories will help prevent the over-extension of AI-generated conclusions into areas beyond their reliability.

Education on AI-contextualization

Scholars and policymakers increasingly highlight “AI literacy” as essential for both professionals and the general public (Celik 2023). By integrating contextualization training into professional curricula—particularly in fields like medicine, law, and engineering—educational institutions can ensure future professionals understand AI’s domain boundaries, confidence metrics, and ethical implications. This measure directly tackles the challenge of integration with professional practice: well-informed practitioners are better equipped to interpret AI outputs, recognize uncertainties, and maintain ethical oversight.

Independent auditing bodies

These are essential for maintaining accountability in evaluating and certifying AI systems regarding their contextualization features. As highlighted in algorithmic accountability scholarship, external audits can uncover hidden biases, promote regulatory compliance, and strengthen public trust in AI systems (Ananny and Crawford 2018; Raji et al. 2020). Establishing such bodies will enhance trust by providing third-party validation of whether an AI system meets established contextualization standards. Certification processes will ensure compliance with best practices and provide transparency to end users, enhancing the credibility of AI systems in sensitive domains.

Looking ahead

The instant expertise paradox and the transformation of knowledge networks represent fundamental challenges in an AI-augmented world. This Comment’s key contribution lies in identifying the need for explicit expertise contextualization within regulatory frameworks, moving beyond traditional approaches that focus solely on technical oversight. While existing regulatory frameworks provide important safeguards, adding Expertise Contextualization as a fourth pillar—alongside transparency, traceability, and human oversight—offers a tangible path forward. This framework establishes clear roles: AI producers implement technical infrastructure for context markers, regulators oversee certification processes, and end-users benefit from clearer system limitations indicators.

As AI continues to democratize access to expert-level outputs, the need for proper contextualization becomes increasingly critical. By implementing Expertise Contextualization frameworks, AI can enhance rather than undermine human expertise, creating a future where artificial and human intelligence work together effectively and ethically. While successful implementation requires organizational practices and collaboration, the regulatory framework proposed here provides the essential foundation for systematic expertise contextualization. This governance structure ensures the democratization of expertise strengthens rather than erodes professional knowledge and judgment.