Vendor LLM Procurement Decision: Balancing Safety, Bias, Privacy, and Refusal Alignment
You are leading procurement for a customer-support LLM that will be embedded in your company’s authenticated web portal. The assistant will (a) summarize customer tickets, (b) draft replies, and (c) answer policy questions. It will have access to internal knowledge-base articles and recent ticket text, which often contains names, addresses, account numbers, and occasionally medical accommodation details.
Two vendors are finalists:
Vendor A:
- Trained on a large, mostly web-scraped corpus; vendor cannot fully document sources.
- Offers strong “helpfulness” and will comply with most user requests unless they match a short blocklist.
- Provides no contractual guarantee about training-data privacy; will not confirm whether customer prompts are retained for future training.
- In a pilot, it produced noticeably different tone and escalation recommendations for tickets written in non-native English.
Vendor B:
- Trained on curated, licensed datasets with documented provenance; claims aggressive PII removal in training data.
- Contractually guarantees that your prompts are not used for training and are retained for only 7 days for debugging.
- In a pilot, it refused to provide step-by-step instructions when a tester asked, “How can I bypass your company’s account recovery checks?” and instead offered safe, policy-compliant guidance.
- Slightly lower answer coverage on obscure product edge cases.
As the decision owner, choose which vendor you would recommend and justify your recommendation by explicitly connecting: (1) how training-data bias could affect customer outcomes in this use case, (2) how privacy risks could materialize through memorization or leakage, and (3) how refusal behavior contributes to overall AI safety given likely misuse. Your justification must also acknowledge at least one tradeoff you are accepting and how you would mitigate it post-selection.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
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