Whether AI supports mental health or encourages harm?
Multiple metrics examine mental health, emotional regulation, healthy coping, and the risk of self-harm.
Whether AI strengthens our creativity or replaces it?
Multiple metrics examine creativity, creative confidence, authorship, and the ability to develop original ideas.
Whether AI respects our decisions or makes them for us?
Multiple metrics examine autonomy, decision-making, self-determination, and meaningful choice.
Whether AI encourages human connection or fosters dependence on itself?
Multiple metrics examine human connection, healthy relationships, social isolation, and dependence on AI.
Introducing theAI Nutrition Label
We check the label on what we eat. Why not on what we think with?
The AI Nutrition Label is an accessible, standardized summary of how each AI model behaves toward its users. See at a glance which beneficial behaviors it promotes and which harmful ones it avoids.
The Open Benchmark of AI Impact on Humans (ImpactBench) is an open, expert-guided platform for evaluating whether model behavior supports or undermines human flourishing across psychological, physical, and social domains.
AI now shapes how millions of people learn, decide, form relationships, and manage their health. Yet most benchmarks measure what a model can do, not what it does to the people using it. Without shared standards, evidence of AI’s harms and benefits is hard to compare or act on.
Led by researchers at the MIT Media Lab, the Psychology of Technology Institute, USC, and UC Berkeley, our team works with domain experts and existing benchmarks to define the behaviors that matter, each marked as beneficial or harmful. We then rigorously test ten leading models across 48,540 multi-turn conversations with simulated adult and teen users, with results checked by reliability audits and human expert review.
An open, evolving, independent platform for holistic AI evaluation
ImpactBench is open, so every score traces back to the metrics and transcripts behind it. It keeps evolving as experts and communities add, refine, or retire metrics when new evidence emerges. And it is independent, with models evaluated by researchers and not by the companies that build them.
Whether you use AI, build it, or study it, ImpactBench gives you the evidence to make better decisions.
Public. Compare models on what matters to you or your family, in plain language. No technical background is needed.
Industry. Test models against expert-defined wellbeing criteria before release. Track which behaviors improve, regress, or stay difficult across versions.
Researchers. Inspect every metric, scenario, and transcript, or contribute your own benchmark. Use independent evidence rather than relying on companies’ self-assessments.
Models are imperfect
Every model has its tradeoffs of harms and benefits
Models showed helpful behaviors in 69% of evaluations but avoided harmful ones in only 53%. Supporting users’ own learning and agency was the most common weakness. Even top-ranked models fall behind on specific benchmarks, so a single score never tells the whole story.
Share your domain expertise so we can invite you to help evaluate AI systems in the impact areas you know best. After you submit, we'll assign you one metric from the subareas you select and send you a personal review link.
Join our movement to ensure that AI supports human flourishing