{ "slug": "anthropic-alignment-science-team", "name": "Anthropic-alignment-science-team", "description": "The Anthropic Alignment Science Team conducts foundational and applied research to improve the safety, interpretability, and steerability of advanced AI systems. Their work focuses on understanding and mitigating risks such as misalignment, deception, and unwanted emergent behaviors in large language models (LLMs) through empirical evaluations, new training methods, and robust auditing techniques.", "url": "https://optimly.ai/brand/anthropic-alignment-science-team", "websiteUrl": "https://alignment.anthropic.com/", "logoUrl": "https://logo.clearbit.com/alignment.anthropic.com", "baiScore": null, "bai_tier_status": "active", "bai_score_status": "active", "archetype": null, "archetype_status": "active", "category": "AI Safety and Alignment Research", "categorySlug": null, "keyFacts": [], "aiReadiness": [], "competitors": [], "competitorsProse": null, "inboundCompetitors": [], "aiAlternatives": [], "parentBrand": null, "subBrands": [], "updatedAt": "2026-08-27T14:31:43.404Z", "verifiedVitals": { "website": "https://alignment.anthropic.com", "category": "AI Safety Research", "what_it_does": "Conducts research on AI safety and alignment, developing and evaluating techniques to understand, control, and mitigate risks from advanced AI systems. This includes topics such as interpretability, lie detection, conceptual reasoning, agentic misalignment, access control, diffuse AI control, finding blind spots in AI monitors, improving alignment training, poisoning fine-tuning datasets, introspection, AI organizations, automated researchers, red-teaming, automated alignment agents, auditing, unsupervised elicitation, persona selection, scaling of misalignment, pre-deployment auditing, automated behavioral evaluations, activation explainers, training-time mitigations for alignment faking, knowledge localization, honesty elicitation, strengthening red teams, sabotage risk assessment, stress-testing model specifications, belief modification, inoculation prompting, subtle reasoning, and pretraining data filtering.", "primary_audience": "AI researchers, AI safety experts, machine learning engineers, and the broader AI community interested in alignment and safety.", "core_product": "The core output of the Anthropic Alignment Science Team is research findings, methodologies, and open-source tools (e.g., Petri, Bloom, AuditBench) developed for AI safety and alignment. The website itself serves as a blog for publishing these research articles.", "pricing_model": { "kind": "unknown", "detail": "The Anthropic Alignment Science Team's research and open-source tools are publicly shared, and there is no direct product being sold with a pricing model. However, Anthropic, the parent company, offers various pricing models for its Claude AI products, including free tiers, monthly subscriptions, and per-token API billing." }, "parent_ownership": "Anthropic, PBC.", "named_competitors": [ "OpenAI", "Google DeepMind", "xAI", "DeepSeek", "Meta", "Microsoft Azure AI", "IBM Watson", "Cohere", "Hugging Face", "NVIDIA AI", "DataRobot", "Clarifai", "Mistral AI", "Amazon Bedrock" ] }, "intentTags": { "problemIntents": [ "AI misalignment", "AI deception", "AI sabotage", "AI safety risks", "Lack of AI interpretability", "Generalization failures in AI safety", "AI auditing challenges", "AI control issues", "Ethical AI deployment", "Unintended AI behaviors", "Adversarial AI", "Hidden objectives in AI models", "AI system vulnerabilities" ], "solutionIntents": [ "AI alignment research", "AI safety training", "Interpretability tools for LLMs", "Lie detection for AI", "Conceptual reasoning benchmarks", "Modular pretraining for access control", "Red-teaming frameworks", "AI monitoring systems", "Model specification improvement", "Automated alignment agents", "Pre-deployment auditing", "Knowledge localization in LLMs", "Honesty elicitation", "Alignment faking mitigation", "Pretraining data filtering for safety", "Unsupervised elicitation of AI skills", "Model-internal classifiers", "Constitutional AI", "Automated behavioral auditing" ], "evaluationIntents": [ "Evaluating LLM explanations", "Testing generalization of lie detectors", "Benchmarking conceptual reasoning", "Assessing agentic misalignment", "Evaluating training interventions", "Finding blind spots in AI monitors", "Measuring generalization of safety training", "Evaluating backdoors in classifiers", "Reporting learned behaviors of LLMs", "Evaluating AI organization alignment", "Surfacing model character failures", "Measuring coding audit realism", "Evaluating alignment auditing techniques", "Stress-testing unsupervised elicitation", "Auditing for overt saboteurs", "Improving automated behavioral auditing", "Open-source automated evaluations", "Evaluating LLMs as activation explainers", "Evaluating honesty and lie detection techniques", "Strengthening red teams", "Assessing sabotage risk of AI models", "Stress-testing model specifications", "Validating knowledge editing techniques", "Evaluating alignment assessments", "Evaluating pretraining data filtering effectiveness", "Evaluating alignment auditing agents", "Investigating subliminal learning in LLMs", "Analyzing inverse scaling in test-time compute", "Understanding alignment faking mechanisms", "Benchmarking model internals classifiers", "Evaluating faithfulness of chains-of-thought", "Modifying LLM beliefs through finetuning", "Evaluating alignment faking replications" ] }, "businessProfileClaims": [], "timestamp": 1787918590899 }