How many audited messages does an always-on assistant need before it can trust a noisy urgency label?
research/ambient-confest
How many audited messages an assistant needs to estimate a noisy urgency label's confusion matrix and prior well enough to act on it: about 50 at moderate accuracy, with smoothing helping at tiny audit sets (stylised)
What it found
Extending the stylised urgency-label model, the assistant estimates the label’s class mix and confusion matrix from n audited messages and then acts by Bayes’ rule. With maximum-likelihood estimates, n = 10 is worse than ignoring the label (mean utility 0.363 against 0.382 at 70% label accuracy); n = 50 recovers 76% of the gain of knowing the true parameters and n = 200 recovers 92%. Add-one smoothing cuts the spread at tiny n but costs a little at high accuracy. Stylised: round-number utilities, uniform label errors, noise-free audit labels.
A figure from its own output
Paper, code and results
More in Ambient AI assistant
- What does a noisy auditor do to an assistant that learns its urgency label’s reliability from audits?What a noisy human auditor does to an assistant that learns its urgency label's reliability from audits: taking audit labels as truth biases the estimate and loses 8% to 69% of the label's gain at a 0.9 to 0.7 auditor, and correcting for a known auditor accuracy recovers it (stylised)
- How many gold-labelled audits replace noisy ones when the auditor’s accuracy is unknown?How many gold-labelled audits replace noisy ones when the auditor's accuracy is unknown: 25 gold audits match 1000 noisy ones read as truth, and a joint EM using 10 gold plus 200 noisy audits nearly reaches the true-parameter policy (stylised)
- How should a fixed audit budget be split between gold-labelled and noisy audits?How to split a fixed audit budget between gold and noisy audits when gold costs 3 to 30 times more: all-noisy is worst or near-worst in all 12 cells, 25% on gold with a joint EM fit is within 0.0009 of the best, and all-gold loses when gold is expensive (stylised)
- Can an assistant learn its urgency label’s reliability from two noisy auditors, and what does a wrongly assumed auditor accuracy cost?Can the assistant learn its urgency label's reliability from two noisy auditors with no accuracy known (Dawid-Skene), and what does a wrong assumed auditor accuracy cost: over-trusting the auditor loses 31% of a 0.9 label's gain, and Dawid-Skene reaches within 0.0005 of the oracle at n = 1000 (stylised)
- What does Dawid–Skene do when the two auditors share their errors?What does Dawid-Skene do when the two auditors share their errors: it over-trusts them (auditor accuracy read as 0.909 for a true 0.8 at half shared) and loses part of its edge over trusting one auditor, falling to equal it when the second auditor is a copy, never below it (stylised)
- What if the two auditors only share errors on hard messages, and can a hardness-aware EM fix Dawid–Skene?What if the auditors only share errors on hard messages: Dawid-Skene's cost is much smaller than with copying (policy utility 0.4060 vs 0.4066 at 30% hard messages), reaching the naive estimator only in an extreme cell, and a hardness-aware EM recovers the classifier but cannot identify the hard share (stylised)
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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.