AI & Technical question
A proposed leaderboard has strong customer pull, but ML researchers believe the benchmark is easy to game and may not correlate with real-world model performance. How would you decide whether to launch, delay, or reject it? Be explicit about what evidence you’d require, what validation steps you’d run, and what minimum trust bar must be met before launch.
- Scale AI
- AI & Technical
- Hard
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What this question tests
Whether you can hold the line on scientific rigor under real commercial pressure and define a concrete evidence bar for a contested launch decision.
How to approach it
- Take the researchers' gaming concern seriously as the default position, since a benchmark's entire value collapses if it does not correlate with real capability.
- Require concrete evidence before launch: a gaming-resistance analysis (can a model overfit to the benchmark's format without real capability gain) and a correlation study against known real-world performance signals.
- Run a small-scale validation, for example testing the benchmark against a handful of known strong and weak models to see if scores track expected relative performance.
- If gaming resistance is weak but customer pull is strong, consider a narrower interim launch, for example an internal or limited-access version while the benchmark is hardened, rather than a full public launch.
- Set a minimum trust bar explicitly: no public launch without a passing gaming-resistance check and a plausible correlation result, regardless of customer pressure.
- Communicate the decision to customers with the evidence, not just a delay, so the relationship is preserved even if launch is postponed.
What a strong answer includes
- Refuses to let customer pull override a failed gaming-resistance or correlation check, protecting the leaderboard's core credibility asset.
- Proposes concrete validation steps, testing against known strong and weak models, rather than an abstract 'do more research'.
- Offers a middle path, a limited or internal interim launch, instead of a binary launch-or-reject decision.
- Communicates the evidence-based reasoning to the customer, preserving trust even under a delay.
Common mistakes
- Launching anyway because of customer pull without addressing the gaming concern.
- Rejecting the benchmark outright without exploring a narrower interim option.
- No concrete evidence bar, leaving the decision as a subjective judgment call.
Likely follow-up questions
- What would you do if the correlation study is inconclusive rather than clearly good or bad?
- How would you communicate a delay to a customer who was promised this benchmark?
- How would you harden the benchmark against gaming without delaying it indefinitely?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Making AI Product Decisions
- Chapter 8: Designing Evals for AI Products
- Chapter 6: Designing AI Agents and Tooling