Ship It Safely
Before an AI feature goes in front of staff or customers: the ways it can hurt someone in a regulated business and how to stop them, how to design it so people can check and correct it, and how to keep it working after launch.Responsible AI for a cannabis business (measure, mitigate, operate), designing AI features people trust (sources, confidence, control, feedback), and the lifecycle after launch: evaluation, monitoring, model updates and cost.Harm identification and layered mitigation, trust-centred interaction design, and the LLMOps loop: evaluation gates, monitoring, pinned models, incident response and governance.
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Using AI Responsibly in a Regulated BusinessResponsible AI in a regulated business: measure, mitigate, operateResponsible deployment: harm identification, layered mitigation, operational controlsDoneDonedone
Before an AI feature goes live, list the ways it could hurt a customer, a worker, or your licence, test for each one, and decide what stops it. You get a one-page harm register to fill in, and the four places to put a safeguard.The three-step routine for putting AI in front of people in a regulated industry: measure the harms (with test inputs), mitigate them in layers (model, safety checks, instructions, the screen, human sign-off), and operate it responsibly after launch. With a harm register to fill in.Harm identification with adversarial test cases, a layered mitigation model (model selection, safety system, system prompt and grounding, UX, human-in-the-loop), and operational controls: reporting, incident response, re-evaluation on change. Cannabis-specific harm classes throughout.
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Designing AI Features People TrustDesigning AI features people trust: sources, sureness, control and feedbackTrust-centred AI UX: explainability, calibrated uncertainty, control, feedback, and automation biasDoneDonedone
An AI feature people can trust shows where its answer came from, says when it is not sure, lets a person fix it before anything happens, and makes it easy to report a mistake. You will take apart one good screen, piece by piece.What makes staff trust an AI feature the right amount: showing sources (explainability), flagging uncertainty, keeping the person in control of every action, and collecting feedback. Plus the opposite failure: people who stop checking because it is usually right.Designing for appropriate reliance: provenance and citations, surfacing calibrated confidence per field, draft-first and reversible actions, lightweight feedback capture, and countering automation bias with targeted review and audits.
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Keeping It Working After LaunchKeeping an AI feature working after launch: the loop, the signals, the rules for changeLLMOps: evaluation gates, monitoring, version pinning, rollback and incident responseDoneDonedone
An AI feature can get worse without anyone touching it: the vendor updates the model, your products and rules change, people use it for new things. Here is the loop that keeps it working, the five things to watch each week, and the rules for making any change safely.Why AI features drift after launch (model updates, changing data, new uses, cost creep), the build-test-watch loop that keeps them working, the weekly signals to watch, and the change rules: pin the model, change one thing at a time, re-test before release, keep a way back.The LLM application lifecycle (explore, build and evaluate, operate, under governance), drift sources, a minimal monitoring set (quality, harm reports, abstention, cost, latency), eval gates on every change, version pinning, canaries, rollback, and a kill switch.