What does a noisy auditor do to an assistant that learns its urgency label’s reliability from audits?
research/ambient-noisyaudit
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)
What it found
Extending the stylised urgency-label model, the audit labels are themselves wrong. Taking them as truth biases the estimated label accuracy (0.57 instead of 0.70 with a 0.8-accuracy auditor) and does not improve with more audits: the policy loses 47% of the label’s gain over ignoring it at label accuracy 0.7, and 31% at 0.9. A 0.9 auditor costs only 8–14%. Maximum likelihood over the unseen true class, given the auditor’s accuracy, recovers the true-parameter value, but it needs that accuracy known (not identifiable from one auditor) and is no better at 50 audits with a poor auditor. Stylised; both label and auditor are made-up uniform-error matrices.
A figure from its own output
Paper, code and results
More in Ambient AI assistant
- 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)
- Does a third auditor identify the hard-message share, and does it help plain Dawid–Skene?Does a third auditor identify the hard-message share: yes at the population level (P(hard) 0.300 from informed starts), only loosely at n=1000 (sd 0.07), and in the extreme cell plain Dawid-Skene with three auditors falls below the naive estimator (0.3912 vs 0.3992) while the hardness-aware EM does not (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.