Governing Mental-State Inference: Source-Neutral Regulatory Triggers and Tiered Obligations
- URL: http://arxiv.org/abs/2608.00936v1
- Date: Sun, 02 Aug 2026 02:28:40 GMT
- Title: Governing Mental-State Inference: Source-Neutral Regulatory Triggers and Tiered Obligations
- Abstract summary: UNESCO Recommendation on the Ethics of Neurotechnology is non-binding guidance.<n>This article develops a source-neutral trigger for technologically mediated, person-linked mental-state attribution.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Two systems can supply the same person-linked attribution to the same institutional decision maker yet fall into different legal categories: one uses neural signals, the other text or behaviour. A source-bound rule therefore permits circumvention, while an all-purpose category of "mental data" risks treating fallible outputs as facts about the mind. This article reads the 2025 UNESCO Recommendation on the Ethics of Neurotechnology as non-binding guidance and develops a source-neutral trigger for technologically mediated, person-linked mental-state attribution. Through selective critical synthesis, conceptual engineering, functional legal comparison, and matched counterfactual cases, it separates elicitation, attribution, and use as cumulative objects of regulation. The analysis also distinguishes two harm pathways from two independently assessed duty series. Seven ordered questions and two escalation predicates assign permitted practices to three duty tiers. Presumptive prohibition is reserved for materially autonomy-affecting non-consensual closed-loop intervention and for specified combinations of covert or coercive inference, core attributes, consequential decisions, and manipulation. Common entry does not erase source-related aggravators: invasiveness, embodiment, and closed-loop capacity can add duties or set a higher minimum, while consequential non-neural inference can reach the same tier. The resulting scheme is a classification device, not an empirically validated regulatory outcome.
Related papers
- Which Rules Matter Now? Policy-Centroid Routing Before an Intelligent System Acts [0.0]
Policy-centroid routing creates a layer before adjudication.<n>Several regimes may trigger at once.<n>The paper develops six falsifiable propositions and seven follow-on studies.
arXiv Detail & Related papers (2026-08-31T13:24:47Z) - One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI [0.0]
We show that a remediation applied by one control can change the action, evidence, or context another control evaluates, invalidating that control's earlier judgment.<n>We formalize this coupling and give a remediate-and-regate protocol that restores per-action soundness in the current bounded, idempotent setting.<n>This is a mechanism demonstration on open payload data with a synthetic metadata layer, not a claim about production prevalence.
arXiv Detail & Related papers (2026-08-18T22:24:44Z) - Inferential Capability Does Not Determine Legal Scope [0.0]
Article 3(1) of the Act uses the capability to inferly.<n>The inference never defines, yet governs it protectively.<n>The thesis: inferential capability does not determine legal scope.
arXiv Detail & Related papers (2026-08-11T07:39:53Z) - Constraint-Driven Synthesis of Hyper Petri Nets [46.9047309711542]
This paper addresses the modeling and synthesis of constrained robotic system behaviors using Petri nets (PNs)<n>It investigates how to construct models in which all observable system states satisfy given logical constraints while remaining consistent with executable transition semantics.<n>The proposed method introduces an explicit execution semantics over observable states, induced by admissible (atomic) firing sequences.
arXiv Detail & Related papers (2026-07-24T07:57:19Z) - Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs [54.96236442484317]
We show that hazardous knowledge can be retained in the model and behaviorally gated by a privileged control token.<n>Our method, Token Inoculation, introduces a binding-and-branching approach.
arXiv Detail & Related papers (2026-07-21T02:15:39Z) - Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain [0.0]
Retrieval-Augmented Generation has become a standard architectural response to unreliability in legal AI.<n>We argue that these failures are symptoms of an architectural mismatch between probabilistic retrieval and the hierarchical, temporal, and institutional structure of legal knowledge.<n>We develop the argument in three moves. First, we articulate the ontological commitment of legal knowledge as a triad of properties derivable from classical legal theory.<n>Second, we identify three corresponding pathologies of retrieval (mereological blindness, diachronic blindness, and causal opacity), each developed with an operational definition, a failure mechanism, a canonical example, and detection criteria for
arXiv Detail & Related papers (2026-06-08T16:46:53Z) - Interpretation, Learning, and Empathy as One Constraint: A Residual-Adequacy Architecture with Accountable Abstention [0.0]
We develop a small cognitive architecture in which limits arise from a single quantity.<n>An Interpretation-Decision Unit (IDU) interprets a content vector through a family of regimes and decides which actions it licenses.<n>We prove the unit is total and deterministic: for any content and fixed configuration it halts in finitely many bounded-cost steps with a unique terminal witness.
arXiv Detail & Related papers (2026-05-24T10:57:28Z) - PRISM Risk Signal Framework: Hierarchy-Based Red Lines for AI Behavioral Risk [3.7184769644515896]
Current approaches to AI safety define red lines at the case level: specific prompts, specific outputs, specific harms.<n>This paper argues that red lines can be set more fundamentally -- at the level of value, evidence, and source hierarchies that govern AI reasoning.
arXiv Detail & Related papers (2026-04-13T06:50:23Z) - Towards Selection as Power: Bounding Decision Authority in Autonomous Agents [0.0]
We propose a governance architecture that separates cognition, selection, and action into distinct domains and models autonomy as a vector of sovereignty.<n>We evaluate the system across multiple regulated financial scenarios under adversarial stress targeting variance manipulation, threshold gaming, framing skew, ordering effects, and entropy probing.<n>Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity.
arXiv Detail & Related papers (2026-02-16T10:10:47Z) - In Quest of an Extensible Multi-Level Harm Taxonomy for Adversarial AI: Heart of Security, Ethical Risk Scoring and Resilience Analytics [0.0]
Harm is invoked everywhere from cybersecurity, ethics, risk analysis, to adversarial AI.<n>Current discourse relies on vague, under specified notions of harm, rendering nuanced, structured, and qualitative assessment effectively impossible.<n>We introduce a structured and expandable taxonomy of harms, grounded in an ensemble of contemporary ethical theories.
arXiv Detail & Related papers (2026-01-23T17:44:05Z) - Noninterference Analysis of Irreversible or Reversible Systems with Nondeterminism and Probabilities [42.52342528033571]
Noninterference theory supports the analysis of secure computations in multi-level security systems.<n>In a nondeterministic setting, assessing noninterference through weak bisimilarity is adequate for irreversible systems, whereas for reversible ones branching bisimilarity has been recently proven to be more appropriate.<n>We recast noninterference properties by adopting probabilistic variants of weak and branching bisimilarities for irreversible and reversible systems respectively.
arXiv Detail & Related papers (2025-01-31T16:49:42Z) - Making Large Language Models Better Reasoners with Alignment [57.82176656663245]
Reasoning is a cognitive process of using evidence to reach a sound conclusion.
Recent studies reveal that fine-tuning LLMs on data with the chain of thought (COT) reasoning process can significantly enhance their reasoning capabilities.
We introduce an textitAlignment Fine-Tuning (AFT) paradigm, which involves three steps.
arXiv Detail & Related papers (2023-09-05T11:32:48Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - Non-Determinism and the Lawlessness of Machine Learning Code [43.662736664344095]
We show that the effects of non-determinism, and consequently its implications for the law, become clearer from the perspective of reasoning about ML outputs as distributions over possible outcomes.
We conclude with a brief discussion of what work ML can do to constrain the potentially harm-inducing effects of non-determinism.
arXiv Detail & Related papers (2022-06-23T17:05:34Z) - Nested Counterfactual Identification from Arbitrary Surrogate
Experiments [95.48089725859298]
We study the identification of nested counterfactuals from an arbitrary combination of observations and experiments.
Specifically, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.
arXiv Detail & Related papers (2021-07-07T12:51:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.