Automation index
How much of a Probation officer’s job can AI already do?
A reporting calendar comes earlier. Stopping reoffending and calling it when it turns do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Probation officer
24%
AI can already do 24% of this job.
#261 / 623
View rankingProbation 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 |
|---|---|---|---|
The order plan, reporting and the conditions.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Visit, interview and what they do not say.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Reports to the judge and the breach.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Network, work and the resource that is not there.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The victim, the offender and not mixing roles.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Call it in time, and not dressing the follow-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- probation
- order plan
- reports to the judge
- breach of conditions
- follow-up visits
- sentence conditions
- reoffending
- periodic reporting
Sources for this job· O*NET 21-1092.00· ESCO/ISCO 2635
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