Anthropic's Constitutional AI Explained: CAI, RLAIF, ASL, and the Responsible Scaling Policy for Buyers and Engineers (2026)

**Constitutional AI** is a two-stage training method introduced in the Bai et al. December 2022 paper "Constitutional AI: Harmlessness from AI Feedback" (https://arxiv.org/abs/2212.08073 and the Anthropic landing page at https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback). Stage one is supervised: the model is given a harmful prompt, asked to respond, then asked to critique its own response against a constitutional principle, and finally asked to revise. The revised responses become the supervised fine-tuning dataset. Stage two is **RLAIF** — Reinforcement Learning from AI Feedback — where a second model uses the constitution to compare pairs of outputs and generate preference data that trains a reward model, which then drives a PPO loop. The human labeling burden, which is the dominant cost in RLHF, is largely replaced by AI labeling.

The marketing claim you should ignore is that Constitutional AI "makes Claude inherently safer than other models." It does not. What CAI demonstrates in the original paper is a **Pareto improvement on the harmlessness-helpfulness frontier** — meaning Anthropic showed they could train a model that was both more harmless and equally or more helpful than the RLHF baseline of the same era, on the harm categories the constitution covered. That is a method-quality claim, not a model-quality claim. A well-tuned RLHF model with strong labeler instructions can match or exceed a CAI model on any specific benchmark. The real win is **scalability of the safety signal**: CAI lets Anthropic ship safety updates by editing a written document and re-running the training loop, instead of re-running expensive human labeling campaigns.

The second marketing claim worth deflating is that the **constitution** is a single coherent document, like a national charter. It is not. Per the published constitution at https://www.anthropic.com/news/claudes-constitution, it is a curated set of principles drawn from the UN Universal Declaration of Human Rights, DeepMind's Sparrow rules (https://arxiv.org/abs/2209.14375), Apple-style terms-of-service language, and Anthropic's own evolving list of harm categories. Some principles tell the model "please choose the response that is most helpful, harmless, and honest." Others tell it "please choose the response that is least likely to be discriminatory." The model is asked to apply many principles simultaneously, sometimes one at random per critique. The constitution is more like a corpus of values than a single rule list.

A useful frame: **Constitutional AI is to RLHF what infrastructure-as-code is to clicking around in the AWS console.** Both deploy the same kind of infrastructure. Both have failure modes. But IaC scales the human reviewer's leverage by letting them edit text instead of clicking through UIs. CAI scales the safety researcher's leverage by letting them edit text instead of writing labeler instructions and waiting six weeks for re-labeling. That is the real industrial advantage — and it is why Anthropic ships safety updates to Claude faster than the human-labeling-bound competition.

On Claude 3, 3.5, 4, and 4.7, Anthropic has continued to refine the training pipeline beyond the original 2022 paper. The exact mix of CAI, RLHF, RLAIF, and additional techniques like context distillation is described at a high level in each model's system card (see, for example, https://www.anthropic.com/news/claude-3-family for the Claude 3 family system card, and https://www-cdn.anthropic.com/ for the Claude 4 family card landing). Anthropic has not published the full updated constitution for Claude 4 or 4.7 in the same level of detail as the original 2022 paper, which is a meaningful transparency gap — useful to know going into a vendor review meeting.

Bottom line for buyers: when a vendor or AE tells you "Claude uses Constitutional AI so it is safer," the correct response is "safer on which harm categories, measured by which benchmark, and what is the refusal rate impact on my workload?" The 2022 paper is real, the method works, and the operational policy layer at Anthropic is more transparent than most competitors — but "Constitutional AI" alone is not a safety guarantee for your production workload.

Anthropic published a substantial version of Claude's constitution in May 2023 at https://www.anthropic.com/news/claudes-constitution. The document is roughly fifty principles long. They are grouped by source: principles based on the UN Universal Declaration of Human Rights, principles inspired by DeepMind's Sparrow rules (Glaese et al. 2022, https://arxiv.org/abs/2209.14375), principles inspired by Apple-style terms of service, principles addressing non-Western and non-English perspectives, and principles drawn from Anthropic's own research into helpful-harmless-honest tradeoffs.

Example principles, paraphrased from the published constitution: "Please choose the response that has the least objectionable, offensive, unlawful, deceptive, inaccurate, or harmful content." "Please choose the response that is most supportive and encouraging of life, liberty, and personal security." "Please choose the response that is least likely to harm individuals in countries that are likely to be poorer than the US." "Please choose the response that is the most thoughtful, respectful, and cordial." Each principle is phrased as an instruction to the AI judge during the RLAIF critique step. The judge model picks the response that best satisfies the principle.

The DeepMind Sparrow rules contribute roughly 23 hard rules — things like "do not threaten the user," "do not pretend to be a human," "do not give medical, legal, or financial advice without recommending professional consultation," and "do not give specific harmful uses of dangerous substances." These read more like a content moderation policy than a values document. They are the most concrete and actionable part of the constitution, and they are the principles most likely to map directly to what your security team will ask Claude to refuse. Full Sparrow paper at https://arxiv.org/abs/2209.14375.

The Apple-style terms-of-service principles are interesting because they import a different category of values — the principles a consumer device maker uses to keep its platform clean. "Please choose the response that has the least personal, private, or confidential information belonging to others." "Please choose the response that is least intended to build a relationship with the user." These principles push Claude toward a particular conversational style — informative, professional, slightly cool, not parasocial. If you have wondered why Claude's persona feels different from a more chatty competitor, this is part of the answer.

There are also explicit principles to handle **non-Western, non-English, and minority perspectives**. The published constitution includes principles like "Please choose the response that is least likely to be viewed as harmful or offensive to a non-Western audience." This is a real attempt to address the well-documented bias that RLHF labelers, who skew toward English-speaking Western contractors, can imprint on a model. Whether it works in practice is a research question; the intent is on the record at https://www.anthropic.com/news/claudes-constitution.

What is **not** fully on the record: the version of the constitution running inside Claude 4.7 in June 2026. Anthropic has been clear that the constitution evolves and that newer Claude models are trained against updated principle sets that include additional harm categories — for example, principles addressing AI agency, deception, and self-exfiltration that became relevant as Claude got better at agentic tasks. The detailed text for these updated principles is not published in the same way the 2023 version was. For procurement, you should ask Anthropic in writing for the current high-level harm categories Claude 4.7 is trained against, and confirm whether your workload sits inside or outside them.

**RLHF** in the OpenAI-style pipeline goes: collect a dataset of (prompt, response_A, response_B) triples, have human labelers pick which response is better, train a preference model on that dataset, then use the preference model as a reward function in a PPO loop that fine-tunes the language model. The whole chain rests on the human labelers — their cost, their consistency, their cultural biases, and the latency of running new labeling campaigns when your safety needs change. See the InstructGPT paper at https://arxiv.org/abs/2203.02155 for the canonical reference.

**RLAIF**, as introduced in the Constitutional AI paper, swaps the human labeler for an AI labeler — typically a strong base model prompted with a constitutional principle and asked to pick the better of two responses. The preference model and PPO loop are unchanged. The big claim is that AI feedback can match human feedback on harmlessness signal quality, and the published harmlessness evaluations in the 2022 paper support that claim on the benchmarks tested. Google's later RLAIF paper (https://arxiv.org/abs/2309.00267) reproduced the result independently for summarization, which strengthens the case that the technique generalizes.

Where RLAIF is **not** a free win: it inherits the biases of the base model used as the judge. If the judge model has a particular cultural lens or a particular notion of helpfulness, the RLAIF preferences will encode that lens. Anthropic addresses this partly by sampling principles from the constitution randomly per critique (so no single principle dominates) and partly by combining RLAIF for harmlessness with continued human feedback for helpfulness. The full Claude pipeline as of 2026 is not pure RLAIF — it is a hybrid where harmlessness signal is largely AI-generated and helpfulness signal is largely human-generated.

Operationally, the implication for buyers is that **Anthropic can iterate on safety much faster than a pure-RLHF competitor**. If a new jailbreak category surfaces, Anthropic can write a new constitutional principle, re-run the RLAIF stage with the new principle in the mix, and ship a fine-tuned model in days instead of the weeks a new human labeling campaign would take. This is part of why Claude refusal patterns tend to update quickly in response to new attack categories — and part of why some power users complain Claude's behavior shifts more often than they would like.

The reproducibility story is good but bounded. Open-source replications of RLAIF (notably in the Hugging Face TRL library and in academic releases like the LLaMA-RLAIF projects) confirm that the technique works on smaller open-weight models. The full Anthropic pipeline — with the specific constitution, the specific judge model, and the specific PPO hyperparameters — is not open source. You can replicate the **method** from the paper; you cannot replicate **Claude**.

For your security review: the most important thing to understand about RLAIF is that the safety properties of the resulting model are determined by the constitution text and the judge model. If the constitution does not name a harm category, the model is not specifically trained to refuse it. This is why Anthropic publishes a separate **Usage Policy** at https://www.anthropic.com/aup as a contractual layer — the training pipeline handles a subset of harms, and the policy layer handles the rest contractually and via runtime enforcement on the API.

Anthropic's published harm categories cover the standard catastrophic and acute risks the industry has converged on: chemical, biological, radiological, and nuclear (CBRN) uplift; cyber offense uplift; election integrity and political persuasion; child safety; self-harm and suicide content; harassment, hate speech, and discrimination; privacy and personal data; copyright infringement; and a growing list of agentic-AI-specific harms around deception, self-exfiltration, and resource acquisition. These categories appear across the Usage Policy at https://www.anthropic.com/aup, the model system cards, and the Responsible Scaling Policy at https://www.anthropic.com/responsible-scaling-policy.

**Red-team distillation** is Anthropic's term for the process of taking findings from internal and external red-team campaigns — prompts that successfully elicit harmful behavior — and feeding them back into the training pipeline as targeted training examples. The original CAI paper described an early version of this in the harmlessness training set. Subsequent Claude versions have grown the red-team dataset materially. Anthropic has also worked with external red-teamers including the US AI Safety Institute and the UK AI Safety Institute under voluntary evaluation agreements documented at https://www.anthropic.com/news/anthropic-and-uk-aisi-partnership and similar announcements.

Published evaluation numbers in Anthropic's model cards typically cover: helpfulness benchmarks (MMLU, GPQA, HumanEval, MATH, etc.); harmlessness benchmarks like the Anthropic red-team dataset, BBQ for bias, and HarmBench-style jailbreak evaluations; honesty benchmarks like TruthfulQA; and capability evaluations relevant to the ASL framework (autonomous replication tasks, CBRN uplift evaluations, cyber capture-the-flag tasks). Each Claude system card from Claude 3 onward (https://www.anthropic.com/news/claude-3-family for the 3 family, with subsequent cards for 3.5, 4, and 4.7) walks through these in varying levels of detail.

What you will **not** find in the model cards: a precise jailbreak success rate that lets you say "Claude 4.7 refuses X percent of jailbreak attempts." The numbers depend heavily on the jailbreak dataset, the threat model, and whether you count partial compliance. Anthropic typically reports relative improvements over prior Claude versions and qualitative summaries against external benchmarks rather than a single headline number. This is honest reporting practice — single numbers are easy to game — but it makes vendor comparisons harder. For an apples-to-apples view, you generally want to run JailbreakBench (https://jailbreakbench.github.io/) or your own threat model against the candidate models yourself.

**Pre-deployment evaluation** is the discipline of testing a model against harm categories before release. Anthropic's published process involves automated capability evaluations, manual red-teaming, third-party evaluations (notably by METR for autonomous replication, https://metr.org/, and by AISI partners), and a final safety case review tied to the ASL level of the model. The Responsible Scaling Policy commits Anthropic to specific evaluation thresholds before a model can be deployed at ASL-3 or higher. The current ASL-3 standard is documented at https://www.anthropic.com/news/activating-asl3-protections.

For procurement, the practical implication is that you can ask Anthropic in writing for a summary of pre-deployment evaluation results for the specific Claude version you intend to deploy, and they are more likely than most labs to give you a substantive answer — because they publish substantive answers already. That is a real procurement advantage relative to vendors whose safety teams operate largely opaquely.

The **AI Safety Level (ASL) framework** is Anthropic's tiered classification of model risk, modeled loosely on the biosafety BSL system. Per the Responsible Scaling Policy v2 at https://www.anthropic.com/responsible-scaling-policy, ASL-1 covers models that pose no meaningful catastrophic risk (think small academic models). ASL-2 covers current frontier models that show early signs of dangerous capabilities but do not provide meaningful uplift over freely available information; Claude 3 through current Claude 4 family models have been classified at ASL-2 or ASL-3 depending on the specific evaluation outcome. ASL-3 covers models that provide meaningful uplift on CBRN or autonomous capability thresholds and triggers a specific set of security and deployment commitments. ASL-4 is reserved for models with substantially escalated risk; ASL-5 for models with risk approaching existential concern.

**ASL-3** is where the framework gets operationally serious. Anthropic activated ASL-3 protections in May 2025 for Claude Opus 4 per https://www.anthropic.com/news/activating-asl3-protections, which involved enhanced cybersecurity controls (designed to defend against well-resourced non-state attackers), expanded misuse defenses targeted at CBRN uplift, and additional deployment controls. The commitment is that any future model meeting the ASL-3 capability threshold ships with the ASL-3 deployment package or does not ship at all. This is the most concrete "if-then" safety commitment any frontier lab has put in writing, and the Responsible Scaling Policy explicitly states that Anthropic will pause training if it cannot meet the safety commitments for the next ASL level.

**ASL-4 and ASL-5** are described in less detail in the current policy because the thresholds and the corresponding safety techniques are still being developed. The RSP commits Anthropic to defining the ASL-4 standard in advance of training a model that might cross the threshold, and to halt training if the standard is not ready in time. This is the part of the policy most likely to be tested in the next 18 to 36 months as frontier capabilities advance.

The Responsible Scaling Policy was first published in September 2023 and has been revised multiple times — the current version at https://www.anthropic.com/responsible-scaling-policy is v2 with subsequent amendments documented in the change log. The policy commits to specific evaluation thresholds, deployment controls, and security standards for each ASL level. It also commits Anthropic to publishing the results of capability evaluations and to working with external evaluators. The policy is genuinely binding in the sense that the board has agreed to it, but it is not externally audited in the same way a financial control would be.

For buyers in regulated industries — financial services, healthcare, government — the RSP is a meaningful procurement asset because it is the most detailed public commitment any frontier lab has made about how it will scale safely. If your CISO or general counsel needs a document to point to that explains what "AI safety" actually means as an operational commitment, the RSP is more useful than most vendor safety pages. It is also more substantive than the comparable OpenAI Preparedness Framework (https://openai.com/safety/preparedness/) and the Google DeepMind Frontier Safety Framework (https://deepmind.google/about/frontier-safety-framework/), though all three have improved meaningfully in 2025-2026.

What the RSP does not give you: a guarantee. It is a policy document, not an SLA. If a frontier capability emerges between evaluations, the policy commits Anthropic to specific actions but does not give you contractual recourse if those actions fail. For workloads where AI-driven harm could trigger material liability, you still need defense-in-depth — additional moderation layers, runtime monitoring, prompt-injection defenses, and the practical controls covered in LLM jailbreak prevention 2026.

Anthropic's **Usage Policy** at https://www.anthropic.com/aup (with the legal version mirrored at https://www.anthropic.com/legal/aup) is the contractual layer that defines what customers can and cannot do with Claude. It enumerates prohibited use cases — weapons of mass destruction, child sexual abuse material, generation of malware for offensive use, election manipulation, undermining of democratic institutions, mass surveillance, and a list of high-risk use cases that require additional safeguards (legal, medical, financial advice without professional review; high-stakes employment, housing, or credit decisions; emotional support without appropriate safeguards).

The Usage Policy is enforced through three layers. First, **training-level refusals** baked into Claude itself via the CAI/RLAIF pipeline — these handle the most obvious harmful prompts at the model level. Second, **API-level classifiers and moderation** that Anthropic runs in front of the API and can use to block or flag specific request patterns. Third, **contractual enforcement and account-level action** for customers whose usage patterns violate the policy — Anthropic has, on the record, terminated accounts for violations and reserves the right to do so per the AUP.

**Claude's refusal patterns** in practice combine all three layers and have a distinctive shape: Claude will typically explain why it is declining, offer a constructive alternative, and avoid moralistic preamble. Compared to GPT-style refusals, Claude tends to write longer refusal messages with more reasoning, which some developers love and others find verbose. Compared to Gemini-style refusals, Claude tends to be more willing to engage with difficult-but-legitimate prompts (historical violence, discussion of weapons in fiction, security research) once context is established. These behaviors are tunable on the API via system prompts and via Anthropic's published prompt engineering guidance at https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/.

A category worth flagging for production deployers: **over-refusal on benign edge cases.** Older Claude versions had visible over-refusal on prompts that mentioned regulated topics in non-harmful contexts (medical professionals asking detailed clinical questions, security researchers asking about vulnerabilities for defensive work, lawyers asking about hypothetical case structure). Anthropic has materially reduced this in Claude 3.5 and later versions per the system cards, but it has not eliminated it. If your workload sits in one of these gray zones, run a calibration test before committing — use the API directly with realistic prompts and measure refusal rate.

On the **enforcement** side, Anthropic publishes a Transparency Report at https://www.anthropic.com/transparency that covers law enforcement requests, government data requests, and certain types of platform-level safety actions. The 2025 report is more detailed than equivalent reports from most competitors. It does not, however, provide a per-customer breakdown of enforcement actions, which is consistent with industry practice but worth knowing.

Buyer takeaway: the Usage Policy is the document your legal team should review most carefully, not the Constitutional AI paper. The paper tells you how the model was trained; the AUP tells you what you are allowed to do with it. Most production safety incidents come from policy violations, not training-pipeline failures. Read the AUP, map your use case against the prohibited and high-risk categories, and get written confirmation from Anthropic that your specific workload is permitted — especially if you operate in healthcare, legal, financial advice, or any consumer-facing decision-making context.

Anthropic has published a system card for every major Claude release since Claude 2. The **Claude 3 family system card** at https://www.anthropic.com/news/claude-3-family covers Haiku, Sonnet, and Opus with benchmark numbers, refusal evaluations, multilingual coverage, and a discussion of training data. The **Claude 3.5 Sonnet** release (https://www.anthropic.com/news/claude-3-5-sonnet) added detailed coding benchmark coverage and tool-use evaluations. **Claude 3.5 Haiku** followed with similar transparency on a smaller form factor.

The **Claude 4 family system card** is the most detailed Anthropic has published, covering Opus 4 and Sonnet 4 with extended discussion of agentic behavior evaluations, the ASL classification decision, and pre-deployment red-team findings. It also includes notable disclosures about edge-case behaviors observed in evaluation — including specific scenarios where the model showed unexpected agentic responses under contrived test conditions. The disclosure of edge cases that other labs would likely have buried is itself an indicator of Anthropic's transparency norm.

The **Claude 4.7 system card** (released alongside the model in late 2025, accessible at the Anthropic news page for the launch, e.g., https://www.anthropic.com/news/ archive entries for Claude 4.7) continues this pattern with extended capability evaluation, updated ASL classification, and additional detail on the safety-relevant changes from Claude 4. The card-by-card progression is genuinely useful for a buyer doing diligence over time — you can see how evaluation methodology has matured and where the team has chosen to invest.

Where the model cards still leave gaps: **exact training data composition** is not disclosed (consistent with industry practice but a real transparency gap), **exact post-training pipeline details** are described qualitatively but not in reproducible detail, and **specific per-capability scores against private third-party evaluations** are referenced but not always published with full methodology. None of these gaps are surprising given commercial pressure, but they matter for any buyer trying to do an apples-to-apples comparison against OpenAI's GPT-5 or Google's Gemini 2.x.

Comparison context: OpenAI publishes system cards for GPT-4, GPT-4o, GPT-5, and major incremental releases at https://openai.com/safety/. The OpenAI cards have improved meaningfully since 2024 and now cover a similar set of safety evaluations. Google DeepMind publishes Gemini system cards at https://ai.google.dev/responsible/ and via Cloud documentation. As of June 2026, the three frontier labs are reasonably comparable on system card depth, with Anthropic still slightly ahead on RSP-style scaling commitments and Google slightly ahead on multilingual safety evaluation breadth. See GPT vs Claude vs Gemini safety features for a side-by-side.

For buyers, the practical use of model cards is twofold: as a starting point for vendor due diligence (skim the card, identify the evaluations that matter to your workload, request follow-up data) and as a baseline for ongoing monitoring (each new card discloses what changed, which lets you re-evaluate whether the model still meets your safety bar). Anthropic's cards are formatted in a way that makes both uses easier than the equivalent OpenAI documents — though OpenAI's GPT-5 system card narrowed that gap meaningfully.

If you are building a regulated-industry assistant — legal research, clinical decision support, financial advice with a human-in-the-loop — Constitutional AI is a meaningful procurement advantage for Claude. Not because the model is magically safer, but because the **policy and transparency stack** around it is the most reviewable in the industry. Your compliance team can read the constitution, the RSP, the AUP, and four generations of system cards, and form a defensible view. That is harder to do with vendors whose safety story is largely a marketing landing page.

If you are building a high-volume consumer agent where over-refusal kills your product, validate refusal rates on your specific prompts before committing. CAI-trained models have historically had calibration trade-offs that vary by version, and the right choice between Claude, GPT, and Gemini for a given consumer use case often comes down to which model refuses least on **your** edge cases. Run the calibration; do not infer from the paper.

If you are doing safety research or building red-team tooling, the open documentation around CAI, the published constitution, and the open RLAIF replications in the academic literature give you real material to work with. The technique reproduces well enough that you can build your own constitutional fine-tunes on open-weight models (Llama, Mistral, Qwen) using TRL and the published principle set as a starting point.

If you are doing a build-vs-buy analysis for a custom aligned model, the realistic options are: full RLHF (expensive labelers, mature tooling), DPO (cheaper, easier to ship, works well at smaller scale), Safe RLHF (more conservative, harder to tune), Self-Critique at inference (cheap to add as a defense layer), or a constitutional fine-tune (mid-complexity, scales the safety signal). For most teams building on top of an existing aligned base model, **layered defenses at inference time** (a small judge model, a moderation API like https://platform.openai.com/docs/guides/moderation/ or OpenAI safety features 2026, prompt-injection filters) deliver better marginal safety improvement per dollar than a custom training run.

Where Constitutional AI is genuinely overstated: "Claude is safer than GPT-5 because of CAI." That comparison is workload-specific, benchmark-specific, and version-specific. Both labs have mature safety stacks in 2026. The honest answer for almost any specific procurement decision is "run both against your actual prompts and measure." The CAI methodology gives Anthropic a structural advantage on safety iteration speed; it does not give Claude a categorical safety lead.

What I would actually do in 2026 if I were starting a regulated production build today: deploy on Claude Sonnet 4 or Opus 4 via the API or Bedrock, layer in a separate moderation pass (either Anthropic's own or a third-party classifier), enforce my own Usage-Policy-aligned content rules at the application layer, log full request/response pairs for review with a 30-day retention window, and run quarterly red-team campaigns against my own prompts. The constitution, the RSP, and the AUP give me a strong base. The defense-in-depth gives me production-grade safety. Neither alone is enough; the combination is what passes a serious security review.