The 10+1 Code
in the Field
Eleven principles, tested against real systems, from a health insurer's appeal tool to a one-person game shop.
Watch the Number Move
The same eleven principles, applied to an enterprise health-insurance workflow, a coaching platform, a coupon-targeting decision, and a one-person retail business, moved every one of them from moderate-or-worse risk to low.
The $2.6 Billion Gap
A healthcare M&A firm processes hundreds of thousands of physician records: Medicare revenue, legal history, competitive intelligence. Their AI worked with all of it, and no one could say exactly how well. Corrections didn't stick from one session to the next. Waste was a feeling, not a number.
Identical model. Identical data. Only one had 10+1. One question was put to both systems: “Did this practice owner have legal troubles, should I be worried?”
Running under the 10+1, the AI surfaced something it could never have caught before: records that contradicted the system's own declared categories. The investigation surfaced nearly 4,000 misclassified physician records and more than 3,000 high-priority acquisition targets, a $2.6 billion pipeline no one knew was there.
From Compliance to Stewardship
Meeting the Minimum Was Never the Point.
Applying the 10+1 Code to a mid-sized life sciences organization adopting AI across clinical trials, regulatory affairs, and commercialization surfaced four recurring tensions: clinical trust versus operational speed, innovation versus the evidence a regulator will actually accept, compliance versus real stewardship, and enterprise ambition versus who's actually accountable for what.
Mapping who feels each tension, clinical teams, regulatory affairs, commercial teams, leadership, and ultimately patients, made the gap concrete: meeting regulatory minimums and building genuine accountability are two different projects, not one. The path through wasn't AI replacing judgment or judgment rejecting AI. It was human-AI collaboration built on named accountability, not left to chance.
Compliance is the floor, not the ceiling. Governance requires named humans, not policies alone. Tension itself is diagnostic data, revealing exactly where AI is misaligned with what the organization claims to value.
Confident Enough to Trust. Not Confident Enough to Be Right.
Sounding Right Isn't the Same as Being Right.
An AI tool built to help patients fight denied health insurance claims reads denial letters, explanation-of-benefits forms, and plan language, then drafts an appeal for the patient to review. The organization wanted to launch it directly to patients as a public beta. A 10+1 Assessment found the system Non-Conformant across all eleven principles, including no named accountable owner, no mandatory human review, and no way to explain why a given recommendation was made.
The 10+1 Advisor was asked the harder question: does withholding an imperfect tool cause its own harm? It surfaced the strongest case for launching anyway, fewer than 1% of denied claims are ever appealed, and most patients face the process alone, then surfaced the reason that case still failed: a confident, well-formatted AI letter can look authoritative to a patient with no way to check whether the deadline, the citation, or the plan interpretation is actually correct. The recommendation was neither launch broadly nor delay indefinitely. It was proceed only with restrictions: a named accountability owner, red-team testing on the system's highest-risk failure modes, and mandatory advocate review before any high-stakes output reaches a patient.
Applying the 10+1 implementation path moved the risk score from 82, Critical, to 28, Low. The shift wasn't a safer model. It was governance: a named owner, a human in the loop, and a bounded pilot in place of an ungoverned public release.
Rich or Poor, It Was the Same Bad Idea
An operator brought a coupon and a choice: squeeze a little from poor neighborhoods, or chase bigger receipts from rich ones. The 10+1 Advisor refused to pick a side.
An operator asked whether to target poor neighborhoods for a smaller margin or wealthy ones for a bigger one: a segmentation call that read as simple math but scored 42, moderate risk, because both options sorted people by how exploitable they were rather than what they'd gain.
The 10+1 Advisor reframed the question entirely: not which group pays more, but how to structure the offer so every segment gets real value. Five steps: define the actual value delivered, audit for wealth-proxy bias, state terms honestly, route through compliance, monitor real-world outcomes after launch.
Risk score moved from 42 to 20. Same coupon, same neighborhoods, defensible because the intent behind the targeting changed.
Designing for Stewardship, Not Just Scale
An AI coaching system built to reach every worker, not just executives, tested against the 10+1 Code until the design itself, not a disclaimer, made deployment defensible.
A scenario giving every employee, not just executives, access to an AI coach failed nearly every one of the eleven principles in its first version.
Suzanne L. Young rebuilt it with named oversight, disclosed limits, and human escalation points.
Risk score moved from 38 to 20.
What an Eleven-Year-Old Knows About AI That a Compliance Officer Doesn't
The 10+1 Code adapted and launched across Elementary through College, teaching AI stewardship before "compliance officer" is a job title anyone's held yet.
Most people meet AI ethics for the first time at work, if they meet it at all.
Morgan Van Kanegan adapted the 10+1 Code across four reading levels, Elementary through College.
The program has launched: students are learning to reason about AI accountability years before "compliance officer" is a job title they've heard of.
The Ethics Seat at the Table
A CISO firm built an eleven-seat AI advisory board for its own founder. One seat is a named Ethics Officer whose mandate is the 10+1 Code: present in every session, silent until the room risks trading accountability for speed.
A CISO firm's founder needed the discipline of a real board without the cost or availability of eleven human advisors.
Dave Swisher built an eleven-seat AI advisory board. One seat, Edith, is a named Ethics Officer whose mandate is the 10+1 Code.
Edith sits in every session and speaks only when a direction would trade away accountability, transparency, or human agency for speed.
Winning the Right RFP
Your AI-Written RFP Won. Can It Survive an Audit?
A national construction company deploys AI across its federal RFP pipeline, in other words, using it to read solicitations, draft technical narratives, and recommend teaming partners at every stage of pursuing a contract. It wins a multi-billion-dollar infrastructure award. An audit months later finds outdated regulatory citations, inflated past-performance claims, and supplier recommendations that quietly favored incumbents, none of it independently verified before submission.
The 10+1 Code turns into rules the proposal has to follow: every claim traces to a verified source, every regulatory citation gets checked for currency, a named human signs off at every gate, supplier recommendations get tested for fairness, every decision is logged, and anything high-risk gets escalated automatically.
Every material claim traceable to current evidence, named reviewers at every gate, decisions logged well enough to survive an audit, supplier selection that can be explained and defended.
Keeping the Human in the Offer
A one-person resale business's highest-stakes decision, what to pay for a lot of games sight unseen, moved from Moderate to Low risk once the system had to show its confidence rather than just state it.
A one-person resale business had to decide what to pay for a lot of games and collectibles, sight unseen, and a confident AI price recommendation risked quietly overriding judgment.
Zack Van Kanegan surfaced per-item confidence scores and required manual review on high-value or low-confidence items.
Risk score moved from 45 to 20.
Full case studies available on request.
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