AI Risk Tiering
Categorizing AI use cases by risk level.
"Not every model needs the same governance."
What is AI Risk Tiering? Categorizing AI use cases by risk level. Governance proportional to risk.
EU AI Act risk categorization.
Governance proportional to risk.
Tier your AI portfolio. Govern accordingly.
Pick what to reward the model for.
Categorizing AI use cases by risk level. In the wild: EU AI Act risk categorization.
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
Pick a reaction to AI Risk Tiering
One tap. We'll point you at the most useful next surface based on how this hits.
The full taxonomy entry
Every concept in the Atlas uses the same structure — so AI Risk Tiering can be compared, recombined, and cited like an element on a periodic table.
- Business
- Leadership
- Government
- Healthcare
- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would AI Risk Tiering most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards AI Risk Tiering?
- If we removed every payoff for AI Risk Tiering, what behavior would replace it?
- Who benefits when AI Risk Tiering persists — and who pays the cost?
- People defend the status quo using the language of ai risk tiering.
- Decisions cluster around the easiest narrative rather than the strongest evidence.
- New data changes the slide deck but not the decision.
- Anyone naming the pattern is treated as the problem.
Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.
See AI Risk Tiering through 3 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Do you actually know AI Risk Tiering?
Three quick questions. Result is saved into your review streak — come back when the term is due to lock it in.
Which best describes AI Risk Tiering?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
When you encounter AI Risk Tiering, your amygdala tags it as threat before your reasoning brain even knows what happened — and threat wins the first move.
Threat detection, fear, social pain, loss aversion, fast emotional tagging. Loss feels roughly twice as bad as equivalent gain feels good. Social rejection lights up the same circuits as physical pain.
See Amygdala in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
When the agent acts, who's responsible?
Systematic skew in model behavior across groups.
Testing model behavior on hypothetical alternate inputs.
Adding noise to data to protect individual privacy.
Quantitative measures of model behavior across groups.
Training models across devices without centralizing data.
Send the card, not just the link
A pre-rendered social card with the title, eyebrow, and URL. Copy the link, post it anywhere, or download the SVG for slides.
More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
People stay quiet when they sense their view is minority — even when it isn't.
Below-cost water rights drive overuse in arid agricultural regions.
Discounting algorithmic advice even when superior.
Passion is inversely proportional to the amount of real information available.
Striking pattern that is statistically expected in large samples.
We don't decide once — we narrow, then evaluate, then commit.
Subjects often converge to neoclassical predictions only after many rounds of feedback — not on the first try.
Seeing things only in their conventional use.
Reward systems slowly diverge from the outcomes they were meant to drive.
Relying solely on metrics that are easily quantified while ignoring what matters.
Pareto applied recursively: the top 1% of inputs produces ~50% of the output.
We judge outcomes relative to a reference point, not in absolute terms.