Generative AI encodes the majority's way of knowing as the default infrastructure of knowledge itself as the default infrastructure of knowledge itself, and law must learn to govern at that level of model training.
Abstract
Generative AI does not merely produce biased outputs. It encodes the majority's way of knowing as the default infrastructure of knowledge itself. We call this epistemic subordination. The training process compresses the full breadth of human expression into a single probabilistic model whose statistical baseline reflects the languages, assumptions, and cultural frameworks of the dominant culture. Minority epistemologies are not excluded but absorbed: present in the training data, yet structurally subordinated in the output. The result is not a collection of discrete biases that can be audited and corrected. It is an epistemic condition embedded in the architecture from which all outputs emerge. This unified harm cuts across three legal domains -- anti-discrimination law, cultural and linguistic rights, and democratic viewpoint pluralism -- and each fails to address it for the same structural reason: existing law regulates downstream, at the level of decisions and applications. The remedy must match the site of harm. If epistemic subordination is produced at the level of model training, then law must learn to govern at that level.
The paper formulates the Control Responsibility Principle (CRP), which shifts AI ethics from a focus on what systems know to a focus on how their behavior is governed, who holds authority over that governance, and how such authority is to be justified, distributed, and contested.
The study argues that this suggests the need for formal AI policy, and for teaching AI-critical genre literacy in English for Specific Purposes education, and for teaching AI-critical genre literacy in English for Specific Purposes education.
N. Avsheniuk, Nataliia Seminikhyna, O. Lutsenko et al.· Arab World English Journal· 0 citations
The integration of artificial intelligence into the legislative process is among the most significant—and least examined—constitutional developments of the decade. This article’s concern is not the composition of legislative text ex novo, but the function constitutional theory assigns to parliaments: the scrutiny of the texts and amendments tabled before them. The research question is accordingly how the deployment of AI in the amendment phase affects the capacity of parliaments to scrutinize legislation—and the accountability, transparency, and separation of powers it secures—and whether human oversight suffices to preserve it. The inquiry centres on two Italian systems at opposite ends of a governance spectrum. GEM (Gestore EMendamenti), the Senate’s amendment-management ecosystem, operational since 2016, ranks among the most sophisticated parliamentary AI tools in use; its functions concern the processing, clustering, ordering, and prediction of amendments, not autonomous drafting—a narrow but constitutionally consequential scope. GENAI4LEX-B, a hybrid architecture combining symbolic reasoning with generative models, has been selected for the Chamber of Deputies but not yet deployed; its value is not empirical but architectural, as a counter-model in which safeguards were designed ex ante rather than emerging reactively from practice. The article advances two arguments. First, it identifies four constitutional fault lines in AI-assisted amendment processing—consequential algorithmic error, deliberate disruption through AI-generated flooding, accountability opacity, and epistemic homogenization across branches—while recognizing the countervailing potential to strengthen scrutiny; it then tests human oversight against the empirical literature on automation bias, concluding that nominal oversight is necessary but not sufficient. Secondly, it contends that the European Union AI Act leaves a significant gap for the legislative process, and proposes a five-element governance framework to ensure that the distinction between algorithmic assistance and algorithmic authorship is maintained through enforceable standards rather than self-regulation alone.
Francesco Gangi Chiodo· Statute Law Review· 0 citations
This work provides language that delivers the requisite analysis, outline mechanisms that subvert trust in scaffolded public communications, and outline machinery for auditing and redressing trust breaches arising from subverting the auditability of inferential chains.
This Article argues that legitimacy is an autonomous regulatory objective, distinct from alignment and not secured by it, which seats consequential AI rule-setting in venues a polity already treats as authoritative.
