Automation index
How much of a Criminologist’s job can AI already do?
A crime map comes earlier. Understanding why it happens there, and what to do with the repeat offender, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Criminologist
29%
AI can already do 29% of this job.
#57 / 623
View rankingCriminologist
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 |
|---|---|---|---|
Data, maps and the victimisation report.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Profiles, reoffending and programme evaluation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Field, interviews and what the spreadsheet misses.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Police, judges and the measure under debate.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Prevention, the neighbourhood and the programme that does not fit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The opinion, and not selling magic recipes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- crime maps
- study reoffending
- victimisation
- evaluate programmes
- situational prevention
- criminological profiles
- reports for judges
- fieldwork
Sources for this job· O*NET 19-3041.00· ESCO/ISCO 2632
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