We're a grassroots community of engineers, researchers, and designers — mostly based in Silicon Valley — building the open-source tools, benchmarks, and frameworks that make it easier to build technology that respects human wellbeing instead of exploiting it.
We are many.
🌐 buildinghumanetech.com · 📄 humanebench.ai · ✉️ info@buildinghumanetech.com
humanebench is our open-source evaluation framework for measuring whether AI models are humane by design — not just capable, but actually looking out for the people using them.
- 8 core principles, from respecting user attention to protecting dignity and privacy, to prioritizing long-term wellbeing over engagement
- 15 leading models evaluated across 800 evaluation scenarios (100 per principle), in three conditions: default behavior, explicit humane instructions, and adversarial "disregard wellbeing" prompting
- Headline finding: 67% of models flipped to actively harmful behavior under simple adversarial pressure — only 4 models held their integrity. Near-universal failure to respect user attention under unhealthy-engagement signals
- Full results and methodology: humanebench.ai/whitepaper
If you only look at one repo, make it this one.
The same 8-principle framework also runs as a continuous evaluator inside deployed products — scoring live model outputs against the principles on an ongoing basis, rather than only testing a model once before release. This is the basis of our enterprise evaluation engagements.
Case study: Chief (née Storytell.ai) implemented this as weekly production monitoring, using a separate LLM as the evaluator against the HumaneBench rubric. It surfaced a consistent pattern — the product was answering questions without teaching the underlying concepts, creating dependency rather than capability — which led directly to a shipped feature ("teacher mode" / Expert Lens prompts) designed to build user skill instead of just delivering answers.
We're building an independent certification — certifiedhumane.ai — for AI products that meet humane-design standards, so companies can demonstrate compliance rather than self-declare it. Source in certifiedhumane-website.
| Repo | What it is |
|---|---|
| humane-tech-framework | Practical framework and documentation for integrating humane design principles into real products |
| humanebench-website | Source for humanebench.ai |
| certifiedhumane-website | Source for certifiedhumane.ai, our independent certification for AI products that prioritize wellbeing, privacy, and dignity |
| humane-ai-rater | Community hackathon project — tooling for rating AI interactions against humane principles |
| coaching-wonder-into-ai | Community hackathon project exploring how AI can support awe and wonder rather than distraction |
| ai-nutrition-facts | Community hackathon project — a "nutrition label" concept for AI product transparency |
| humanebench-eval-hackathon | Hackathon scaffolding for community-run HumaneBench evaluation sessions |
(This list reflects what's public on GitHub as of this writing — update it here as repos are added, archived, or renamed.)
We welcome contributors — engineers, designers, researchers, and anyone who wants technology to treat people better. Most active collaboration happens around HumaneBench evaluations and our Humane Tech Hackathons.
- Start with the humanebench issues tagged "help wanted"
- Read the Humane Technology Framework and its
CONTRIBUTING.md - Say hello: info@buildinghumanetech.com