These visualizations are experimental and still being refined. Designs and data presentation may change.
Actor Misalignment
Comparing expert ratings (responsibility or vulnerability per actor) vs governance targeting frequency (how many governance documents actually target each actor). Toggle between responsibility and vulnerability to see where governance attention doesn’t match expert expectations.
How to read this
Each row represents one of 7 actor types with two dots: governance targeting (green) and the selected expert metric (responsibility or vulnerability), connected by a line whose length encodes the misalignment. Positive gaps mean the expert rating exceeds governance targeting. Both metrics are min-max normalized to 0-100% for visual comparison. Hover over any row for raw values.
Key Takeaways
- 1.AI Infrastructure Provider has the largest responsibility gap at +16pp — experts expect far more from this actor than governance documents currently target.
- 2.AI Governance Actor is the most over-targeted at -19pp — governance documents address this actor more than expert responsibility ratings suggest.
- 3.Of 7 actor types, 3 are under-targeted and 2 are over-targeted by governance relative to expert responsibility assessments.
- 4.AI Governance Actor receives the most governance attention (572 docs), making it the primary focus of current regulatory frameworks.
Expert metric:
Governance Targeting
Expert Responsibility
Both metrics are min-max normalized to 0-100% for visual comparison.