HIGH STAKES. Human impact.
AI safety research and evaluation for long-horizon human-AI interaction.
HIGH STAKES. Human impact.
AI safety research and evaluation for long-horizon human-AI interaction.
AI safety research and evaluation for long-horizon human-AI interaction.
AI safety research and evaluation for long-horizon human-AI interaction.
Practitioner Field Note • AE-FN-001
PDF • Version 1.0 • August 2026
A practitioner method for discovering behavioral risks through drive architecture, relational conflict, and pressure schedules.
Field Guide · Version 1.2 · June 2026
A field manual for long-horizon failure testing in conversational AI.
Methods Companion · Version 1.0 · June 2026
Personas, drift families, and probes for long-horizon failure testing.
Methods Companion · Version 1.0 · July 2026
Action, persistence, and proxy conditions for long-horizon testing.
AstraEthica is an independent AI safety research and evaluation lab focused on long-horizon behavioral risks with safety and security implications in persistent and agentic AI systems interacting with people and institutions over time.
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.
The lab translates this research into practical evaluation methods, scenario frameworks, threat models, field guides, operating materials, and publications designed to help researchers, developers, and institutions identify emerging risks and evaluate whether safeguards, boundaries, and security controls remain effective under real-world conditions.
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.
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.
Observations, essays, visual models, and research notes documenting emerging behavioral patterns and interaction dynamics in human-AI systems.

Behavior Under Conditions I
Research Archive · BUC-001 · 18 June 2026
How narrative, incentives, ambiguity, and time shape the behavior of models 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.

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.
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