AI-Mediated Public Decision-Making and Democratic Exclusion: Governance Risks and Accountability Frameworks
Università Magna Græcia di Catanzaro, Italy
Abstract
Artificial intelligence is increasingly embedded within public administration, shaping welfare allocation, migration control and regulatory enforcement. Existing debates on AI governance have primarily focused on bias mitigation, transparency and risk-based compliance. While these approaches have advanced oversight mechanisms, they remain largely oriented toward managing harm at the level of system performance. This article advances a distinct analytical claim: democratic exclusion in AI-mediated public decision-making is infrastructural rather than merely output-based.
By conceptualising AI systems as governance infrastructures, the paper argues that exclusion may arise from the architectural embedding of optimisation logics within public authority. Algorithmic systems can pre-structure access pathways, recalibrate discretion and redistribute justificatory responsibility prior to individual decisions. These transformations may not be fully captured by bias detection or rights-impact assessments.
Drawing on constitutional principles and governance theory, the article identifies structural risks associated with epistemic asymmetry, automated filtering and procedural compression. A comparative analysis of European Union and United Kingdom regulatory trajectories demonstrates that both risk-based and principle-based frameworks remain predominantly compliance-oriented. The paper concludes by proposing democratic inclusion safeguards that operate at the level of institutional embedding, emphasising domain-sensitive justification, substantive oversight and inclusion monitoring. Democratic resilience in algorithmically mediated governance depends not only on technical robustness, but on preserving the justificatory foundations of public authority.
Keywords:
Algorithmic governance; democratic exclusion; public administration; accountability; algorithmic regulation; EU AI ActData Availability Statement
This study does not involve the use of empirical datasets. All sources supporting the findings of this research are publicly available and cited within the manuscript.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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