Automation index
How much of a Court-appointed expert’s job can AI already do?
Measuring, photos and a first report come earlier. Confirming it in court does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Court-appointed expert
26%
AI can already do 26% of this job.
#208 / 623
View rankingCourt-appointed expert
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 |
|---|---|---|---|
Accept the appointment, collect the file and plan the survey.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Inspection, measurements and samples.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Write the report, annexes and replies to challenges.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Confirm in court and answer the parties.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Coordinate with the procurator, the court and other experts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Records, fee assessment and liability.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Inspection, measurements and samples
- Confirm in court and answer the parties
- Records, fee assessment and liability
- perito judicial
- perito
- forensic expert
Sources for this job· O*NET 19-4092.00· ESCO/ISCO 3119
IS WORK OVER?
ML engineer
29%
#64 / 623
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