Product Managers use it to turn vague “responsible AI” expectations into a staged intake, a written brief, and a prioritized plan that can survive stakeholder scrutiny. ML Engineers get a concrete way to surface failure modes and mitigations tied to deployment context (not just model metrics). Risk, Compliance, or Privacy Leads use it to document conditional claims, affected populations, and accountability boundaries in a format leadership can review. AI Consultants and Auditors apply it to run consistent client intakes and produce a Montreal Declaration–aligned report without reinventing the framework each time.

Healthcare and digital health teams use it to assess patient harm pathways, informed consent gaps, and what human responsibility looks like when clinicians rely on AI output under time pressure. Financial services and fintech apply it to map fairness and autonomy risks in lending, underwriting, fraud, and collections, where false positives and opaque decisions can create real hardship. HR tech and hiring platforms use it to identify disparate impact risks, proxy variables, and governance needs when employers operationalize model outputs. Public sector and civic tech find it valuable for systems that affect democratic participation and civil liberties, especially where surveillance, identity, or eligibility decisions can impact non-consenting people.

A typical prompt like “Write me an ethics review for my AI product” fails because it: lacks a multi-stage intake that adapts to risk level and scale, provides no Montreal Declaration structure to keep the analysis complete and consistent, ignores affected populations beyond direct users, produces generic advice (“be transparent,” “avoid bias”) instead of a prioritized risk register and action plan, and misses boundaries like “What This Is NOT” plus provisional language when details are unknown.

Yes. The prompt is designed to adapt its stage count and depth based on your risk level, deployment context, and affected populations, even if you only provide partial details at first. To customize, be explicit about the decision being supported (advisory vs automated), the environment (consumer app, workplace tool, government program), and what happens after the model outputs. A strong follow-up is: “Treat this as high-stakes; expand to 12 stages, then ask only the top 10 clarifying questions that would change the risk ranking.”

The biggest mistake is describing only the model and skipping the real-world decision chain; “We built a classifier” is weak, while “Its score auto-triggers account suspension unless an agent overrides within 24 hours” is actionable. Another common error is naming only “users” and omitting indirect groups; “customers” is vague, but “non-account household members recorded in background audio” changes privacy and consent analysis. Teams also under-specify data provenance, saying “public data” instead of “2021–2023 scraped forum posts plus purchased demographics,” which affects fairness and democratic participation concerns. Finally, people treat missing info as fine; it’s better to answer the prompt’s targeted follow-ups and accept a provisional assessment until the unknowns are resolved.

This prompt isn’t ideal for one-off copywriting tasks where you just need a quick disclaimer, or for teams that refuse to provide basic deployment details and expect a definitive “approved” verdict anyway. It’s also not a substitute for legal advice or a formal third-party audit in tightly regulated contexts. If your goal is purely conversion optimization, start with workflow or checkout prompts instead, then come back to this assessment when the system behavior and rollout plan are clear enough to evaluate.