A Shared Language for AI Reliability
Bridging the disconnect between Group A (Governance Teams and Leaders) and Group B (Technical Staff) with a bidirectional taxonomy.
We translate granular failure tags into user-facing syndromes that drive accountability.
You’ve experienced this. Here’s the name.
When AI assistants fail, they don’t fail randomly. They fail in patterns — patterns that repeat across every tool, every task, and every team. Six names for what’s been frustrating you all along.
Deployable artifacts for teams that need to act.
Not just a taxonomy — a working toolkit. RFP language your legal team can use, incident report templates your ops team can run, and deployment checklists that tie directly to measurable syndrome thresholds.
All templates are free, CC BY 4.0, and adaptable to your organization’s regulatory environment and risk tolerance. Interactive matrix explorer and calibration guidance available via Supplementary Materials.
Help validate the framework. Get co-authorship credit.
The Core Six needs independent coders to confirm it holds up. The inter-rater reliability study is open to anyone with AI experience — technical or otherwise.
- On launch: First-cohort platform access
- On release: Co-authorship credit on the IRR results paper
- Full coded dataset access after publication
- Early access to all YIM Project research

