Writing
I write about who absorbs the failure when an automated decision is wrong, and what it takes for the correction to reach them.
10 selected here, out of 230+ at The Crumple Zone · RSS feed
Start here
- The Official Record Is Late A structure can be correct on paper and dangerous in practice — here is where that observation came from, and what I’m building now
- If Every User Is a Potential Threat People are not becoming dishonest. They are becoming game-theoretically optimal for the environment they have been placed in.
- Toothless Ethics: Why Principles Don’t Stop Machines A guide to the difference between moral language and structural constraint
More essays
- The Stop Button Was Removed Toyota let any worker call for a stop, and a stopped line restarted only once the problem was fixed. Administrative systems took the stop button out. Put it back.
- People Are Not the Error-Correcting Layer Why apparent performance is always designed performance plus unaccounted human compensation.
- Nobody Points “Next Best Action” at Themselves Theory of Constraints, career-capital theory, RICE scoring, decision journals, and XP gamification, run as one script instead of five separate books.
- Don’t Let Reassurance Do Engineering’s Job Why “we care” substitutes for obligation — and how delay gets disguised as kindness.
- You Don’t Have the Right In this Age of Appeals, you have the paper right. And a stamina test.
- How to Design for Cognitive Scarcity Stop designing for the idealized “Hero User.” Build resilient interfaces that work when your user is stressed, tired, and operating on 15% battery.
- Pending: The Political Economy of Waiting The loading screen is the most powerful weapon in the modern state
How it works in practice
The essays argue the case. These pages describe the practice: what gets checked before an AI system is used, how its evidence is kept current, and what an institution needs in place to correct a mistake.
- Framework Deciding when an AI system may be used What has to be written down before anyone relies on an AI system: the claim, the evidence behind it, who can challenge it, and what happens when it turns out to be wrong.
- Operating model Approval, correction, and recourse The four stages from “not authorized” to “routine use,” the six-step review that runs when evidence changes, and the conditions that take a system back out of use.
- Current work Ambit, Refract, and Fast Harm What an agent has been approved to do, whether its sources have changed since it read them, and whether a correction reaches everyone the error touched — alongside NextConsensus’s evidence work.
- Standards Ethotechnics technical standards Proposed standards covering decision registers, contestability, resolution latency, evidence calibration, delegation records, and runtime revocation.
Recurring questions
- Why does an AI system punish faster than it can explain?
- Who is accountable when a model makes the decision?
- What infrastructure is missing between “AI can do this” and “AI should do this”?
- How do institutions maintain the ability to challenge automated decisions over time?
- The question after “Did it work?”: for whom, by consuming what, and could the people paying that cost make it work differently?
I keep writing about one pattern: people with less power absorbing the uncertainty, friction, and failure created by people with more control. Read the research program →
Revised