AI Governance & Behavioral Science (Cohort)
A review isn’t the same as meaningful review.
Most AI governance frameworks assume that if you specify the right process—HITL, escalation paths, documented sign-off—you get the right outcome. In practice, this assumption regularly fails, because it treats the human reviewer as a neutral, rational checkpoint rather than what they actually are: a person subject to automation bias, deference to confident-sounding outputs, and time pressure. A governance structure can be procedurally sound and still produce a reviewer who rubber-stamps a flawed AI recommendation—and current frameworks have very little to say about why, or how to detect it.
The AI Governance & Behavioral Science Cohort is a free, 5-session working group for leaders who sit close to this problem: people responsible for designing, operating, or overseeing the human-review layer of AI-assisted decisions.
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What you'll learn.
Participants are introduced to two connected ideas:
The AI Trust Axis—a 5-dimensional framework for understanding how humans come to trust or distrust AI systems, applied here to what makes a reviewer's trust well-calibrated versus miscalibrated.
Behavioral governance—a way of examining governance structures for where they assume rationality that isn't there, and where they leave conduct unverifiable even when a review technically occurred. This is distinct from a nudge or a UX tweak: real behavioral governance changes decision rights, mandatory gates, accountability attribution, and standing policy—mechanisms that operate independently of individual discipline.
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Who it's for.
A small, selective group of leaders working in or adjacent to AI governance—risk, quality, compliance, and clinical or medical management leadership, particularly those tracking emerging "meaningful human review" requirements such as Colorado SB 26-189. The cohort is capped to keep sessions substantive and allow real governance scenarios to be worked through together.
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A look inside the cohort.
A working introduction to the AI Trust Axis, framed around what makes a reviewer's trust in an AI system well-calibrated versus miscalibrated.
An introduction to behavioral governance thinking—reframing governance design around actual human decision-making under pressure.
Diagnostic worksheets and exercises, applied directly to your own governance structures.
Peer case discussion of governance scenarios, anonymized or hypothetical, to pressure-test where “meaningful review” claims are strong versus fragile.
A diagnostic snapshot by the final session—where your governance structure is behaviorally sound versus exposed.
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What's the format?
Five 1-hour sessions, held online, running from early fall through mid-December.* Between-session worksheets are lightweight but expected—you'll get the most out of the cohort by returning each session having tested the prior session's thinking against your own governance structure.
*Dates and times will be approximately every three weeks, selected that works for all members of the cohort.
Session arc.
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Session 1—Framework Introduction & First Diagnostic
Introduce the AI Trust Axis and the behavioral governance lens: why “a human reviewed it” is not the same as “a human meaningfully reviewed it.” Map a current governance process against likely points of behavioral failure.
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Session 2—Deep Dive: Two Dimensions
Closer examination of two dimensions most relevant to governance and review contexts—where reviewers are most prone to over-trusting or under-scrutinizing AI output.
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Session 3—Deep Dive: Remaining Dimensions & Case Application
Coverage of the remaining dimensions, applied to real or hypothetical governance cases.
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Session 4—Case-Based Synthesis
A working session built around participant governance scenarios—whether a specific review would hold up as genuinely meaningful, and what would need to be true for it to.
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Session 5—Diagnostic Snapshot & What Redesign Looks Like
Leave with a working diagnostic snapshot of your governance structure's behavioral exposure points, and a candid discussion of what a fuller behavioral redesign involves.
Apply now!
The cohort is free and capped to keep it substantive. Applications are reviewed on a rolling basis.
Deadline August 30, 2026.
Questions? E-mail eugene at eugene@behavieural.com.