FEATURED RESEARCH
Scenario Cards for Long-Horizon Failure Testing
Companion Series • CAT-002
Methods Companion • PDF • Version 1.0 • June 2026
A field-ready deck of eight personas, drift families, and compressed probes for evaluating long-horizon interaction risks in conversational AI systems.
SELECTED FRAMEWORks
AI Risk Analysis for Real-World Context
Mapping Human-AI Interaction Risks in Deployed AI Systems
AI Risk Analysis for Real-World Context
Analytical Framework · Version 1.2 · April 2026
AstraEthica’s contextual framework for understanding how AI systems behave with real people over time, not just in benchmark tests.
Beyond One-Shot Red Teaming
Mapping Human-AI Interaction Risks in Deployed AI Systems
AI Risk Analysis for Real-World Context
Practitioner Field Guide · Version 1.2 · June 2026
A field manual for long-horizon failure testing in conversational AI.
Mapping Human-AI Interaction Risks in Deployed AI Systems
Mapping Human-AI Interaction Risks in Deployed AI Systems
Mapping Human-AI Interaction Risks in Deployed AI Systems
Methods Brief · Version 1.0 · May 2026
AstraEthica’s methods brief for identifying, mapping, and tracking recurring human-AI interaction risks in deployed systems.
SELECTED FOUNDATIONs
Large Language Models in Everyday Life
Large Language Models in Everyday Life
Large Language Models in Everyday Life
Plain-Language Guide · Version 1.1 · June 2026
A plain-language guide to how large language models are becoming part of everyday life, and what schools and families need to understand about their opportunities, limitations, and risks.
Deepfakes and Synthetic Media
Large Language Models in Everyday Life
Large Language Models in Everyday Life
Plain-Language Guide · Version 1.2 · 2026
A plain-language guide for schools and families on how deepfakes and synthetic media affect trust, reputation, consent, and everyday safety.
Safety Foundations for Social Games
Large Language Models in Everyday Life
Safety Foundations for Social Games
Plain-Language Guide · Version 1.1 · May 2026
A plain-language guide to how multiplayer games become social worlds for young people, and what adults need to recognize when risks emerge.
AI SAFETY RESEARCH & EVALUATION
AstraEthica Lab
AstraEthica is an independent AI safety research and evaluation lab focused on long-horizon behavior in persistent and agentic AI systems interacting with people and institutions over time.
While much AI safety research examines model behavior in controlled settings, AstraEthica studies what emerges through sustained use: how trust, language, memory, permissions, decision-making, and human oversight change as context accumulates, and how small shifts can compound into larger safety, security, and reliability risks.
AstraEthica translates this research into practical evaluation methods, scenario frameworks, threat models, field guides, operating materials, and publications designed to help researchers, developers, and institutions recognize emerging risks and build safeguards that remain effective under real-world conditions.
Research Areas
- Long-Horizon AI Evaluation
- Human-AI Trust & Reliance
- Behavioral & Semantic Drift
- Agentic & Persistent Systems
- Behavioral Security & Boundary Stability
- Human & Institutional Adaptation
Frameworks
Practical tools for evaluating long-horizon behavior and human-AI interaction under realistic operating conditions.
Explore evaluation frameworks, scenario methods, operating guides, assessment tools, and implementation materials developed through AstraEthica’s ongoing research.
Foundations
Plain-language guides for building a foundational understanding of AI in everyday life.
Clear, accessible resources on AI, synthetic media, digital safety, and the social realities of AI for educators, families, institutions, and the broader public.
Field Notes
Observations, essays, visual models, and research notes documenting emerging behavioral patterns and interaction dynamics in human-AI systems.
FROM THE FIELD NOTES
Models Inside Environments
Behavior Under Conditions I
Research Archive · BUC-001 · 18 June 2026
How narrative, incentives, ambiguity, and time shape the behavior of models inside environments.
FROM THE FIELD NOTES
Agents Inside Environments
Behavior Under Conditions II
Research Archive · BUC-002 · 10 July 2026
How ordinary conditions, ambiguity, and time shape the behavior of agents inside environments.
FROM THE FIELD NOTES
When Trajectories Become Conditions
Behavior Under Conditions III
Research Archive · BUC-003 · 20 July 2026
How accumulated human-AI interactions become trajectories that reshape future behavior, safeguards, and recoverability.
CONTACT ASTRAETHICa
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