Automation index
How much of a Court officer’s job can AI already do?
A summons form comes earlier. Serving it in person so the deadlines do not slip do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Court officer
21%
AI can already do 21% of this job.
#430 / 633
View rankingCourt officer
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 |
|---|---|---|---|
Summons, notices and the day’s list.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Serve in person, attach and the one who will not open.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Courtroom, call cases and the order of the hearing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Custody of exhibits, deposits and the transfer.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The hard address and the note to the secretary.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
That service is on record, and not losing the exhibit.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- serve summons
- attach goods
- call cases
- keep exhibits
- gazetting
- serve in person
- move deposits
- courtroom order
Sources for this job· O*NET 33-3012.00· 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