Meta — Expected Bayesian Loss for Neutral Launches

Meta's Expected Bayesian Loss turns neutral-launch safety from a CI-overlap yes/no into a tunable downside-risk number, matched to experts on 17 experiments.
Key takeaways:

• CI overlap is broken for neutral launches. Containing zero is permissive with scarce/noisy data and oversensitive with abundant data — absence of evidence ≠ harmlessness.
• EBL = P(harm) × severity. Closed form from frequentist m and SE s (Jeffreys prior): EBL = s φ(m/s) − m Φ(−m/s). Noisy experiments get higher loss.
• Expert-matched thresholds. On 17 similar-precision experiments (14 launch / 3 block), τ_B = 100 bp…