Cohort 1 · May–June 2026
The first Responsible AI Professional cohort didn't just leave with a certificate. Twenty graduates built sixteen working governance tools for their own organizations — and here's what they said about it.
“People become less capable evaluators of AI output, precisely as they become more dependent on it.”
Sarah Downey · Guest Instructor
The results
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graduates certified
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governance tools shipped
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shipped a working tool by week 4
0%
would recommend RAP
0%
rated it valuable to their work
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sectors represented
Every certificate is issued through Certifier and publicly verifiable on credsverse.com. Survey figures reflect the Cohort 1 end-of-course feedback responses.
In their words
I came in having already studied AI, but I was looking for something deeper on the ethics side, and this course gave me exactly that. It was honestly amazing. It changed how I think about my work, and I left feeling proud of how I show up with AI.
Brittney Ashley
Founder, people-first consulting & marketing agency · Creative Dynamics
RAP gives you a zoomable drone's-eye view of the entire AI ethics and governance landscape. You learn enough about the tools and frameworks you need to succeed when you're down in the weeds of a specific project, without losing the big picture view that pulls it all together.
Daniel Bashaw
Learning-experience designer
RAP pointed out a wholistic view of AI for me. I was focused on what AI could do to increase productivity, making more sales, etc. Thought that as long as we had an enterprise account and our IT dept signed off on it we were good for security. Viewing AI with this new lens has made me think hard about the ethics surrounding AI usage, and how I now look at developing tools.
Bruce Ratzlaff
Sales & operations leader, industrial sector
I can't say enough good things about this course. The facilitators really knew their stuff, but they also made it easy to follow. The content was well laid out and practical, starting with the basics and building up to things I could actually use. But the best part was the weekly live sessions — lively, honest discussions where people asked real questions and dug in together. That made the whole thing feel less like a lecture and more like a group figuring things out side by side. The Capstone at the end was my favourite part: we each got to build a real, working tool, and the support along the way was fantastic. I finished the course with new skills and something I'd actually built myself. If you're thinking about signing up, do it.
Kerris Hougardy
AI literacy & enablement lead · TING
If you're looking for guidance on how to think about AI regulation within your organization, this is a great course to help you get started.
Manny Minhas
AI governance & identity practitioner
What they built
Each capstone is a working responsible-AI tool a graduate built for their own organization — from HR and publishing to foster care, industrial trades, and health research. Named projects belong to graduates who chose to share publicly; the rest are shown anonymously.
Takes a job description and returns a per-skill risk profile across automability, time horizon, augment-vs-replace, and transferability — plus suggested reskilling paths. Its defining ethical choice: it rates skills and roles, not people.
Built by Kerris Hougardy · AI literacy & enablement lead
View the toolA simple, immediately deployable internal AI policy: four core rules plus four quality-check questions to run before any AI use — Did a person make the final call? Is any client data in here? Are the facts verified? Does it still sound like us?
Built by Brittney Ashley · Founder, Creative Dynamics
Governs the AI tools that decide who gets time, attention, and travel in a sales org — keeping that prioritization explicit, reviewable, and free of patterns you'd be uncomfortable explaining to a customer. Includes an 'idea gate' that stress-tests new AI workflows before the team builds them.
Built by Bruce Ratzlaff · Sales & operations leader
A low-budget retrieval-augmented assistant giving foster caregivers guidance grounded in official provincial documents. Its governance spine is a five-step maturity model — the system won't go live until every dimension reaches its launch boundary.
Built by Daniel Bashaw · Learning-experience designer
A governance matrix that treats autonomous AI agents as non-human identities inside the organization — the access, accountability, and oversight questions you have to answer before an agent acts on your systems.
Built by Manny Minhas · AI governance & identity practitioner
View the toolA founder-stage 'stewardship intelligence' GPT built on the advisor's own professional artifacts, with the course's Critical Thinking GPT run over the result as a counterbalancing bias check. Portable across model vendors; in active use during the current wave of generational wealth transfer.
Shared anonymously
Inverts the automation question: scans a professional's work to flag not just what could be automated, but the work they clearly find energizing and should be protected from automation. Centers employee experience over pure efficiency.
Shared anonymously
Checks an input prompt against a personal AI-use policy and rewrites it into steps, assigning the most cost-effective model per sub-task. A real test ran 73% cheaper, and every run emits a 'receipt' of models, tokens, and cost. It flags but never blocks, and never says 'safe' — only 'no rule has been tripped.'
Shared anonymously
A browser-based (no login, no server, no stored data) intake tool for people decisions: eight questions producing a readiness report with risk tier, plain-language statement, and citations grounded in Canadian law — including a row for what an affected employee can access or contest.
Shared anonymously
A 90-minute team-workshop guide using the NIST framework plus a use-case mapper that classifies each AI use in plain language. Targets a real sector risk — teams feeding unpublished manuscripts and confidential sales data into AI. The work earned a Department of Canadian Heritage invitation to brief policy analysts.
Shared anonymously
Iterated from an AI chat widget to a static Excel/HTML calculator — deliberately moving AI out of the runtime for privacy, focus, consistency, and environmental cost. Conclusion: AI was most valuable in the design phase; a static calculator is the right endpoint.
Shared anonymously
A two-part instrument that separates two decisions people wrongly conflate: is this tool safe to deploy, and is this practice ready to use it? Hard stops for jurisdiction/consent failures; 'Unknown' treated as a risk signal. A live test surfaced a jurisdictional risk a standard feature comparison would have missed.
Shared anonymously
Turns weekly meeting transcripts into per-person action plans and flags organizational drift when the week's work diverges from the strategic roadmap. The governance is the human gating — a lead approves the processing, reports, and tasks before anything reaches the CEO.
Shared anonymously
A 291-page manuscript converted into an interactive web book built for epistemic accountability — a 'suspicion meter' rating citations by backing sources, a fallacy flag, a verification log, and an open-source layer where readers can flag errors.
Shared anonymously
A framework for governing patient data across the intelligence stack in AI-assisted research, built on Indigenous OCAP® principles — ownership, control, access, and possession applied to the health-data lifecycle.
Shared anonymously
Capstone walkthroughs
Public Cohort 1 capstone walkthroughs from graduates who chose to share their work: the responsible-AI decisions, constraints, and artifacts behind the tools.
Consulting & marketing
Walkthrough by Brittney AshleyWatch on YouTube
HR & workforce
Walkthrough by Kerris HougardyWatch on YouTube
Industrial & trades
Walkthrough by Bruce RatzlaffWatch on YouTube
See it live
Not slides. Not mockups. Working tools you can open right now — real output from the responsible-AI systems they built during the four weeks.
Same four weeks. Same small room. Your turn to build the thing you leave with.
