Description:
I need a practical checklist to judge AI vendors’ claims about accuracy, privacy, bias, and integration costs before procurement. What tests, metrics, and pilot designs can I use to validate performance on our data, and what contract terms or questions protect against data misuse and vendor lock-in? Also, what red flags should prompt us to walk away?
2 Answers
Once I bought a subscription to a shiny AI scheduler because it promised to "never double book" and then it sent my partner three invites for the same dinner while also CCing my boss on a personal note, which led to a week of awkward explanations and me hiding receipts in weird places. I mention that because vetting AI is part tech and part gut feeling, and you will want to catch both the math mistakes and the weird social ones.
For practical checks run a shadow test on your live traffic plus a held out dataset that mirrors edge cases, measure precision recall and calibration across subgroups, track drift and latency, and run adversarial or injection tests. Validate privacy with synthetic input attacks, check differential privacy parameters or encryption in transit and at rest, and demand provenance of training data. Pilot in "read only" mode then a phased canary with KPIs and rollback gates. Contractually require data ownership, deletion proofs, audit rights, portability of models and weights, SLA uptime and error budgets, indemnity for data breaches, and exit assistance. Ask for SOC2 reports, third party audits, model cards, and patch cadence. Walk away if they promise perfect accuracy, refuse audits, block exports, or have opaque training data or subcontractor chains.
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