This work proposes DELTΔX, a non-decision AI governance framework where AI is strictly limited to measurement, verification, and conformity checks.
Core principle:
Any AI-assisted interaction must produce a measurable positive delta (ΔX > 0). If no positive delta can be demonstrated, the system must stop or remain silent.
The framework explicitly forbids delegation of intent, judgment, or decision-making to AI systems. Human responsibility remains invariant at all times.
DELTΔX is documented as a fully auditable corpus (27 PDFs), including:
- Explicit stop conditions
- Formal responsibility invariants
- Traceability and verification constraints
- Non-decision operational boundaries
The corpus is published with a DOI and designed for peer review, audit, and critique.
Repository:
https://doi.org/10.5281/zenodo.18100154
Feedback, critique, and adversarial review are welcome.
Top comments (2)
Interesting angle.
Most AI governance proposals focus on alignment of decisions. This framework instead enforces a structural refusal of decision delegation.
Two points I find particularly strong:
Treating ΔX > 0 as a hard operational invariant (not an aspiration) creates an explicit stop condition, which is largely missing from current safety discourse.
Framing AI strictly as a measurement / verification layer preserves human intent and judgment as non-transferable, which is closer to how safety-critical systems are actually certified.
I’m curious how you formalize ΔX across heterogeneous contexts (qualitative vs quantitative gains), and how falsifiability is handled when ΔX is disputed.
Overall, this reads less like “AI ethics” and more like systems engineering applied to governance, which is refreshing.
For clarity: this framework does not claim certification, regulatory authority, or normative status.
DELTΔX is intentionally positioned as an auditable operational layer designed to complement existing AI governance standards by addressing practical gaps (explicit stop conditions, non-decision constraints, traceability, and human responsibility invariants).
The full corpus (27 PDFs) is published with a DOI for independent audit, critique, and adversarial review.
I am particularly interested in feedback on failure modes, edge cases, and real-world applicability limits.