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@keidolabs

Keido Labs

AI Psychology R&D Lab

Keido Labs

AI talks about emotions. We study whether it understands them.

We're an AI Psychology research lab. We publish what we find — the question, the method, the result. Data open. Code open. No hand-waving.

If you're building in this space — or just care about getting it right — everything here is yours to use.

Studies

Paper Status Repo
Keep4o — Empathy Is Not What Changed: Clinical Assessment of Psychological Safety Across GPT Model Generations Published, Feb 2026 keep4o
Whether, Not Which: — A Mechanistic Dissociation of Affect Reception and Emotion Categorization in LLMs Writing up, Mar 2026 affect-receptions
Multi-Provider Safety Eval — Safety Posture and Empathic Quality Across Frontier AI Providers Ongoing, Q2 2026 coming soon

Research programme

Layer 1 — The Shield. Measure whether AI conversations are psychologically safe. Clinical rubrics, validated against expert judgment, deployed at scale.

Layer 2 — The Teacher. Move from observation to intervention. Use monitoring data as training signal — mid-conversation course correction, not hard-coded rules.

Layer 3 — The Breakthrough. Understand the mechanisms of emotional reasoning inside AI. Map the circuits. Build AI where psychological safety is architectural, not bolted on.

Open science

Every study releases its full stimulus set, extraction pipeline, analysis scripts, and reproduction code. Clinical frameworks and rubrics are published alongside papers.

We work with researchers, clinicians, and institutions working on AI emotional intelligence and psychological safety. If you're working on related questions — or want to use our frameworks in your own research — get in touch.

Links


Founded by Dr. Michael Keeman — clinical psychologist, AI systems engineer, interpretability researcher. Liverpool, UK.

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  1. .github .github Public

  2. affect-reception affect-reception Public

    All code, stimuli, and results for a mechanistic interpretability study investigating how large language models internally represent emotional content

    Python

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