Strength of current models on the capabilities this job's tasks require.
Task automation potential33/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption0/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength0/100
Quantity and source quality of the evidence behind the score.
0 evidence item(s) contribute. Calculated 19 hours ago. 12 Oct 2026 UTC.
Overview
Inspects products and processes for defects. Vision systems flag defects reliably in controlled environments; judgement on disposition stays human.
Employment
≈400,000
approx. BLS OES
Median wage
≈$45,000
approx. BLS OES
Tasks tracked
4
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 4/5 · best current capability 78/100
Evidence
Evidence trail
0 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
No occupation-specific evidence has been mapped yet. This occupation is scored from generic task assessment only - a genuinely weaker basis, and the reason its score is marked low-confidence.
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Score · 23 points · 2021-01-01 to 2026-07-01
Reconstruction, not measurement. 14 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
Research published
▲ IMPACT 40
Epistemic Uncertainty-Aware Defect Detection for Quality Control in Medical Device Manufacturing
arXiv:2610.09057v1 Announce Type: new Abstract: Objective: We investigate whether accounting for epistemic uncertainty can improve the reliability of automated defect detection in medical device manufacturing. Methods: We consider a machine learning framework that operates on heterogeneous manufacturing and device-report data represented with Knowledge Graphs. To mitigate errors arising from uncertainty in the decision model, we analyze a principled rejection strategy to abstain from predictions whose estimated epistemic uncertainty exceeds a specified threshold. We evaluate the approach using
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
Occupation score change
▼ IMPACT 90
quality control inspector exposure decreased 9.0 points
AI Exposure Score moved from a previous calculation to 30/100 under methodology v1.0. 0 evidence item(s) contributed.
Verified signal/Confidence:40 · low/Verified by AIKillsWhat
Occupation score change
▲ IMPACT 10
quality control inspector exposure increased 1.0 points
AI Exposure Score moved from a previous calculation to 39/100 under methodology v1.0. 0 evidence item(s) contributed.
Verified signal/Confidence:40 · low/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation
arXiv:2610.00447v1 Announce Type: new Abstract: Can a text-to-3D leaderboard change when every generated scene stays fixed? We audit this question for rendered-image evaluation, where camera settings and caption wording become part of the measurement protocol. Across 300 frozen scenes from six generators, we vary eight render and caption factors for 19 alignment evaluators plus one perceptual-quality control, then test four targeted scene degradations. Peak configuration variance exceeds between-generator variance for 17/19 alignment evaluators, with prompt-bootstrap lower bounds above 1 for 1
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat