Data Literacy for AI & Machine Learning Use Cases Playbook
- Practitioner
- Advanced
- Template Included
- Workshop Ready
A structured method for teaching business users to question AI and ML outputs before acting on them, covering probability, robustness, data origin, bias, and when to escalate to a human.
Do participants need a technical or statistics background for this workshop?
No — PROBE is deliberately designed for non-technical business users. The workshop translates statistical concepts like confidence scores into plain-language frequencies ("wrong 1 in 7 times") rather than requiring participants to understand the underlying math.
How is this different from general AI awareness training?
General AI awareness training tends to cover ethics and high-level concepts; PROBE is an applied checklist tied directly to a specific model or tool participants use in their actual job, with a hands-on exercise where they find a real failure case themselves.
What if we don't have a data scientist available to help run the workshop?
Having someone from the model-owning team present is strongly recommended for the model-card walkthrough and to field technical questions, but the PROBE checklist itself and the escalation-mapping exercise can be facilitated by an L&D or literacy lead using existing model documentation.
How do we decide what triggers mandatory human escalation?
Map it directly to consequence and uncertainty together — high-stakes decisions (credit, hiring, safety) paired with lower model confidence or known blind spots (like Harborview's self-employed applicant segment) are the clearest candidates, and the threshold should be written down explicitly, not left to individual judgment.
Does this playbook apply to generative AI tools like chatbots, or only predictive ML models?
Both — the Origin and Bias questions apply directly to what a GenAI tool was trained on, and the Probability question becomes "how do I know this fluent-sounding answer is actually correct," which the prompt literacy and sanity-check sections address specifically for generative outputs.
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