?asOf= parameter to see the current catalog state.The technical problem of designing AI systems whose objectives, behaviour, and emergent goals reliably track the values or instructions of their principals across deployment contexts.
Definition & scope
Alignment, in the technical sense, is distinct from regulatory 'compliance' or 'safety.' It asks: even if a model is capable and even if it is supervised, does it pursue what its principal actually wants — or does it pursue a proxy objective that diverges in edge cases? The problem decomposes into outer alignment (specifying what we want the model to do — see Krakovna et al.'s 'specification gaming' literature) and inner alignment (whether the model trained on that specification actually internalised it — see Hubinger et al. 2019 on mesa-optimisation). Governance instruments rarely use the word 'alignment' directly. EU AIA Art. 51-55 obligations approximate alignment concerns by mandating systemic-risk assessment + adversarial testing + cybersecurity protection, but do not require demonstrated alignment of model objectives. US EO 14110 §4.2(a) mandated reporting on alignment-relevant capabilities (red-team results) without defining 'alignment.' Anthropic, OpenAI, and DeepMind publish their own alignment research agendas; these are de facto cited in policy debates but absent from binding text. The field treats alignment as a research problem first and a governance object only secondarily.
Locus of dispute: Is the inner-outer alignment decomposition the right frame, or does it presume capabilities (long-horizon planning, model self-awareness) frontier LLMs do not yet have? Pope et al. (2023) vs. Hubinger lineage.
Use in governance
How instruments operationalise this concept
| Instrument | Jurisdiction | Status |
|---|---|---|
| EU AI Act | EU | in force |
| Executive Order 14110 on Safe, Secure, Trustworthy AI | US | partial |
| G7 Hiroshima AI Process Code of Conduct | G7 | in force |
| Anthropic Responsible Scaling Policy (RSP) v2 | US | in force |
| OpenAI Preparedness Framework | US | in force |
| Google DeepMind Frontier Safety Framework | US | in force |
| Singapore Model AI Governance Framework for Generative AI | SG | in force |
Appears in topic articles
Editorial note
Wiki articles referring to 'alignment' in a regulatory context should pair the technical sense with the specific regulator's adjacent vocabulary (EU AIA: 'systemic risk assessment'; US EO 14110: 'safety evaluations'). The technical-alignment literature predates and exceeds the regulatory framings.
See also
Further reading
Sources on the broader topics this concept relates to — complementing, not standing in for, the primary sources cited inline above. 70 academic & grey-literature sources; catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.
- An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI Peer-reviewed✦ AITraces how the AI Act's legal text shifted across versions among the terms 'AI system, general purpose AI system, foundation model, and generative AI', exposing definitional instability in the regime.
- The EU model of AI governance: regulating artificial intelligence through law and policy Peer-reviewed✦ AIAnalyses how the AI Act's risk-based model handles general-purpose and foundation models whose 'autonomous content generation challenges legal categories of authorship, accountability, and control'.
- Generative AI and data protection Peer-reviewed✦ AIExamines friction between foundation-model training and the GDPR, noting models that 'memorize and leak pieces of training data' cannot be treated as anonymous.
- Defending Compute Thresholds Against Legal Loopholes Preprint✦ AIIdentifies 'enhancement techniques that are capable of decreasing training compute usage while preserving... model capabilities', exposing loopholes in compute-reporting thresholds.
- GPTs are GPTs: Labor market impact potential of LLMs Peer-reviewed✦ AIFinds around 80% of the U.S. workforce "could have at least 10% of their work tasks affected" by LLMs, which exhibit "traits of general-purpose technologies".
- Computing Power and the Governance of Artificial Intelligence Preprint✦ AIArgues compute is a uniquely governable lever because it is "detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain".
- Training Compute Thresholds: Features and Functions in AI Regulation Preprint✦ AIFinds "training compute currently is the most suitable metric to identify GPAI models", but thresholds should only trigger further scrutiny, not determine risk measures alone.
- Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe Peer-reviewed✦ AICensus of hyperscale cloud regions shows a divide between "Compute North" states hosting training-relevant compute and a Compute South, shaping who can wield compute-based governance.
- Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity Peer-reviewed✦ AIExamines how the EU AI Act, liability regimes, GDPR, copyright and cybersecurity rules apply to generative AI, identifying gaps and proposing targeted regulatory refinements.
- Evaluating Frontier Models for Dangerous Capabilities Preprint✦ AIPilots dangerous-capability evaluations (persuasion, cyber, self-proliferation) on frontier models, finding 'early warning signs' but no strong present danger — grounding evaluation-based gating.
- Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation Preprint✦ AIArgues 'compute providers should have legal obligations' to secure infrastructure, keep records, verify activity and report frontier training as regulatory intermediaries.
- Verification methods for international AI agreements Preprint✦ AISurveys '10 verification methods that could detect... unauthorized AI training... and unauthorized data centers', mapping the technical basis for compute-disclosure regimes.
+ 58 more across this concept's topics — see the literature index.
References
The primary instrument sources behind the article's classifications.
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