AI Alignment via Incentives and Correction
We study AI alignment through the lens of law-and-economics models of deterrence and enforcement. In these models, misconduct is not treated as an external failure, but as a strategic response to incentives: an actor weighs the gain from violation against the probability of detection and the severity of punishment. We argue that the same logic arises naturally in agentic AI pipelines. A solver may benefit from producing a persuasive but incorrect answer, hiding uncertainty, or exploiting spuriou
Record details
Published: 2 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
arXiv · 5 May 2026
Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents
arXiv · 28 April 2026
Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents
arXiv · 7 May 2026
Structural Enforcement of Goal Integrity in AI Agents via Separation-of-Powers Architecture
arXiv · 26 April 2026
Containment Verification: AI Safety Guarantees Independent of Alignment
arXiv · 9 May 2026
Guided Streaming Stochastic Interpolant Policy
arXiv · 11 May 2026
How to cite this record
ethics.ai (2 May 2026), “AI Alignment via Incentives and Correction,” evidence record 5065, https://ethics.ai/record/5065 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.