- The paper’s main contribution is its argument for transitioning governance from explainability to a validation-centric approach in high-stakes AI systems.
- It proposes a comprehensive framework that includes pre- and post-deployment testing, third-party audits, and harmonized standards to ensure system robustness.
- The study highlights regulatory implications, emphasizing risk-based oversight and liability to improve accountability and fairness in AI deployments.
Beyond Explainability: Centering Validation in AI Governance
Introduction
The increasing integration of Artificial Knowledge (AK) systems into high-stakes sectors such as healthcare, finance, and criminal justice presents acute challenges around opacity, trust, and regulatory oversight. Despite a growing policy emphasis on explainability—anchored in notions of transparency and interpretability—explainability mechanisms have revealed substantial limitations in both technical feasibility and their ability to genuinely bolster accountability. This analysis reviews the case for shifting governance priorities toward validation, understood as the comprehensive pre- and post-deployment verification of AI system reliability, robustness, and consistency, irrespective of their intrinsic explainability. Such a paradigm is argued to be both more pragmatic and more risk-sensitive, particularly in contexts where technical or economic constraints render explainability unattainable or limited.
Limits of Explainability as a Governance Paradigm
Dominant regulatory frameworks and scholarly discourse have conceptualized explainability as a keystone for trustworthy AK systems, predicated on the assumption that human-understandable rationales are essential for meaningful accountability. However, the notion of interpretability remains ambiguously defined and frequently fails to translate into concrete improvements in oversight or user outcomes (Rozen et al., 2023). Contemporary research exposes multiple pathologies of XAI: unreliable, misleading, or oversimplified explanations can spur unwarranted trust ("white box paradox", "XAI halo effect"), mask underlying model deficiencies, or even facilitate user manipulation (Martens et al., 9 Apr 2025). There is increasing empirical evidence that explanations often induce overconfidence in flawed systems or create only the illusion of transparency (Baker et al., 2023, Baker et al., 14 Mar 2025). Moreover, the pursuit of explainability often necessitates model simplifications that can degrade predictive accuracy, potentially undermining fairness—especially in high-stakes settings where errors incur significant social costs.
An over-reliance on post hoc interpretability tools also risks entrenching black-box approaches, as explanations are retrofitted to complex models without substantive scrutiny of their reliability or integrity (Wang et al., 2015, Ndiyavala et al., 2019). Several regulatory frameworks—such as the GDPR and the EU AI Act—have incorporated explainability mandates, but practical implementation frequently founders on technical disconnects and failures of interpretability, particularly for deep learning and other non-linear methods [euaiact2021].
Validation: A Robust Alternative
The validation paradigm shifts focus from model transparency to functional assurance: systematic verification that AK systems consistently perform to established benchmarks of accuracy, reliability, and safety. Validation encompasses empirical, functional, and (where applicable) normative testing in both pre- and post-deployment phases, leveraging stress tests, third-party audits, and harmonized conformance standards. This approach is already prioritized in substantial regulatory instruments, including the US Algorithmic Accountability Act (2022), the EU AI Act (Article 10), and sectoral guidance from the US FDA (GMLP) (Myllyaho et al., 2021).
Validation-centric governance offers several advantages:
- Scalability and Risk Sensitivity: Unlike explainability, which often scales poorly with model complexity and deployment scope, validation protocols can be adapted for opaque but high-performing models, supporting robust performance without necessitating perfect interpretability (Pfau et al., 2024, Banerjee et al., 2020).
- Objectivity: Validation targets observable outputs and empirical reliability, allowing clear benchmarks for fairness and compliance (e.g., preventing discriminatory outcomes in hiring and credit scoring) [euaiact2021, (Myllyaho et al., 2021)].
- Alignment with Safety and Accountability: High-stakes applications (medical imaging, autonomous vehicles, criminal risk assessment) already utilize validation as the primary safeguard, given that explainability may be unattainable for state-of-the-art systems (Pfau et al., 2024).
- Incentivization and Liability: Regulatory frameworks can tether liability and economic incentives to validated performance, internalizing risks and spurring investment in robust validation practices.
Empirically, the growing adoption of validation requirements in the US (O/MB M-25-21), EU, and China illustrates the political and practical consensus around the necessity of robust evaluation independent of model transparency [euaiact2021, (Myllyaho et al., 2021)].
The Validity-Explainability Trade-off: Typological Framework
A central contribution is the typology classifying AK systems along two axes—validity (validated/non-validated) and explainability (explainable/opaque)—yielding four governance-relevant quadrants:
- Valid-Explainable: Interpretable, reliable systems, typically limited to simple or low-stakes applications (e.g., linear regression in logistics).
- Valid-Opaque: Reliable, high-performing but inscrutable models (e.g., deep neural networks in diagnostics), prevalent in state-of-the-art high-stakes applications.
- Pre-Valid Explainable: Transparent but not yet robustly validated systems; suitable for experimental or developmental use, often operating within regulatory sandboxes.
- Non-Valid Opaque: Unreliable and inscrutable systems, which pose maximal risk and justify either strict controls or prohibition.
This matrix clarifies the cost-benefit trade-offs: while hybrid and partially interpretable models are an area of active research (Esmaili-Dokht et al., 2024), regulatory and operational priorities should privilege validation for high-stakes, opaque AK. Nonetheless, explainability remains indispensable where practicable, particularly to safeguard values of procedural fairness, user recourse, and human autonomy.
Regulatory Implications and Future Prospects
Policy responses increasingly reflect differentiated approaches across jurisdictions. While the EU and China recognize both explainability and validity, the US framework (and recent federal guidance) explicitly prioritizes validation as an enforceable, risk-based obligation [euaiact2021, omb2025m2521]. Emerging proposals advocate a multi-tiered governance mechanism, incorporating:
- Pre- and Post-Deployment Validation: Continuous evaluation attuned to operational drift and changes in deployment context.
- Third-Party Auditing and Harmonized Standards: Establishing independent, technically sophisticated validation bodies and internationally harmonized criteria (ISO, OECD) to encourage accountability and reduce regulatory fragmentation.
- Liability and Market Incentives: Robust liability regimes to internalize system risks and incentivize ongoing improvements in validation infrastructure.
- Public Infrastructure and Fairness Metrics: Funding for shared validation resources and standardized datasets, with attention to ensuring accessibility for SMEs.
Theoretical and practical questions persist regarding the limits of output-based validation—especially for novel architectures with emergent behaviors—and the appropriate calibration of explainability requirements relative to application context.
Conclusion
The prevailing focus on explainability as the linchpin of AI trustworthiness is insufficient to meet the challenges posed by highly opaque, yet high-performing AK systems. Regulatory, policy, and technical advances underscore the need to center validation as the primary mechanism for ensuring accountability, robustness, and societal trust—supported by harmonized standards, liability structures, and continuous oversight. While explainability retains essential value for contestability and fairness, especially where its costs are not prohibitive, the future of responsible AK deployment depends on a systematic commitment to rigorous, context-sensitive validation regimes. Future research must continue to refine hybrid models, scalable validation methods for complex AK, and governance strategies that balance innovation with public welfare and institutional trust.