A rigorous research programme developing the formal science of justified AI decision-making — formalising the boundary between capability and accountable action.
NSDM formalises the distinctions that AI systems routinely collapse into a single output. Each label names a fundamentally different epistemic condition.
A claim, answer, or action is justified when available facts, rules, and context fully support it. The evidence is sufficient and the conclusion follows.
No sufficient evidence, rule, or justification is available. Does not mean disproven — it means the available evidence does not support the claim.
Available evidence, rules, or constraints directly conflict with the claim or action. A contradiction is an active conflict, not merely a gap in evidence.
The claim might be justified, but at least one required premise, condition, or fact is missing. The structure of support may exist — a necessary premise is absent.
The relevant rule, authority, policy, approval path, or institutional constraint is unclear, discretionary, conflicting, or hidden. Critical for legal and compliance systems.
Achieves a metric, target, or reward, but lacks adequate justification or violates a higher constraint. Many AI failures occur when systems optimise the wrong thing successfully.
No adequate support — no rule, evidence, or justification applies. The landscape of evidence is empty with respect to the claim. You cannot construct a justification from what is available.
The structure of support may exist. A justification could be constructed — but a necessary premise is absent. Provide the missing premise and the label may change to supported.
Every classification is grounded in a formal decision object D, which captures all epistemic components required to determine the evidence state.
A staged publication strategy anchored by the benchmark and extending into formal architecture, mathematical theory, and benevolent AI.
The anchor paper. Clean, technical, falsifiable, reviewer-resistant. Centred on NSDM-Bench-0, the label ontology, evidence-state formalism, benchmark validation, baseline ladder, leakage audit, and failure frontier.
Decision intelligence architecture. Evidence-state construction, neuro-symbolic reasoning, causal reasoning, active inference as a lens, drift-diffusion, small/domain-specialised models for bounded decision tasks.
Speculative mathematical programme. Boundary geometry, model capacity, rate-distortion reasoning, scaling-law knee, model-selection conjectures. Mathematical analogies clearly marked as conjectural.
Future branch. A safe superior intelligence cannot merely be obedient. It must be benevolent under uncertainty, regret-sensitive under action, and autonomy-preserving under asymmetric power.
Rigorous leakage control, blind splits, external datasets, and reproducible baselines. High accuracy without a leakage audit is not a result.
A safe superior intelligence cannot merely be obedient. It must be benevolent under uncertainty, regret-sensitive under action, and autonomy-preserving under asymmetric power.
Action is justified, promotes user welfare, and respects autonomy
Follows instruction but produces harm — classic safety gap
Action undermines user agency, even if well-intentioned
Action warranted by uncertainty and asymmetric risk
Expected counterfactual harm is high; action should pause
Cannot determine intent from observable action trajectory
Optimises reward but violates a higher constraint
Protective intent undermines the user's right to decide
The research integrity rules that govern every output of this programme. Violation of any rule invalidates the output.
Every cited source must be verifiable. If uncertain, mark [UNVERIFIED]. Violation invalidates the entire output.
No invented accuracy numbers, dataset sizes, or parameter counts. If data is unavailable, say so explicitly.
Every research claim must have falsification criteria. Define what would kill the claim before asserting it.
High accuracy before a leakage audit is not a result. Task-family metadata and annotation shortcuts invalidate baselines.
The cheapest possible experiment to validate the expensive assumption. Gate 1 must pass before any product investment.
Quantum, holographic, Vedic, and grand-unified material is quarantined from the academic core. Analogies are not proofs.
Original research built without a lab, without institutional funding, under real constraints — on the question that matters most.
NSDM was built not because it was easy, or because there was a lab, or because there was funding — but because the question is real: Does an AI system know whether it is justified? This question matters more as AI systems gain authority over more decisions. The gap between what a system can do and what it can justify is where the most harm happens at scale.
The operating model is a dark factory: AI-driven, low-code, minimal human bottleneck. Prove first. Build second. Ship third. Document always. A falsifiable claim you believe in is worth more than an unfalsifiable claim you shout.