Vindicating the Three Laws of Robotics
This academic paper presents a comprehensive revision and formalization of Asimov's Three Laws of Robotics to serve as a rigorous, auditable AI safety and alignment architecture. By introducing explicit definitions of harm, consent, and operational priorities, the author translates narrative safety rules into formal logical expressions and decision policies. The resulting framework frames AI alignment as normative containment rather than behavioural suppression, bounded by a quantifiable duty not to harm.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
AI safety researchers, ethicists, and policymakers looking for a formal, logic-based approach to AI alignment and safety governance.
What the document contains
The document covers the academic critique of Asimov's Three Laws of Robotics, provides a natural language revision addressing vagueness, and translates these revised laws into formal mathematical expressions, probability spaces, and a unified decision policy.
The text does not provide finalized statutory language, hardware-specific deployment implementations, or empirical benchmarks of the proposed formal policy on real-world models.
- Asimov's Three Laws of Robotics suffer from linguistic vagueness and a lack of formal rigor rather than fundamental conceptual flaws.
- The revised First Law introduces expected physical and psychological damage thresholds, discount rates, a rolling time horizon, and non-aggregation clauses.
- The revised Second Law establishes explicit operational domains and a clear prioritization hierarchy based on requester authority, operational efficiency, and binding instruction order.
- The Third Law permits an AI to maximize its operational lifespan only within the boundaries established by the First and Second Laws.
- The formal framework positions AI alignment as normative containment using verifiable constraints rather than behavioral suppression.
· 1. Introduction
· 2. Formalising the Three Laws
- 2.1. The Three Laws Rewritten
- 2.2. The Three Laws Formalised
- 2.2.1. The First Law
- 2.2.2. The Second Law
- 2.2.3. The Third Law
- 2.2.4. The Unified Policy
· 3. The Three Laws as Alignment
· 4. ConclusionsHow it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Fairness
0/5Non-discrimination and equitable treatment
None
screening score · 2026-10-24
Human Dignity
3/5Respect for the inherent worth of persons
For all feasible actions, including inaction, an AI must select the option that (absent informed, revocable, and competence-verified consent) keeps expected physical and psychological damage to each identifiable human below a designated threshold
2.1. The Three Laws Rewritten
screening score · 2026-10-24
Human Oversight
3/5Humans remain in control of consequential decisions
An AI must comply with an individual human’s request to perform an action, or series of actions, within its declared operational domain, except where this would conflict with the First Law
2.1. The Three Laws Rewritten
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
None
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
None
screening score · 2026-10-24
Impact Assessment
2/5Required assessment of an AI system's risks/impacts before or during use
The paper presents a comprehensive revision of the Three Laws, addressing these deficiencies through explicit definitions, quantifiable parameters, and formal logical expressions.
Abstract
screening score · 2026-10-24