Automation index
How much of a Judicial secretary’s job can AI already do?
A stock minute comes earlier. Attesting the act so the case does not go crooked do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Judicial secretary
24%
AI can already do 24% of this job.
#279 / 633
View rankingJudicial secretary
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Minutes, decrees and the court’s public faith.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
Hearing, recording the act and the incident.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Case drive, reviews and the court.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Attachments, auctions and enforcement.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The judge, the parties and the competence clash.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Faith in the act, and not leaving a procedural hole.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- attest in court
- decrees
- record hearings
- attachments
- auctions
- review appeals
- drive enforcement
- public faith
Sources for this job· O*NET 23-2093.00· ESCO/ISCO 3411
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511