Automation index
How much of a Victim support worker’s job can AI already do?
A protocol and an appointment come earlier. Being there when they give evidence, and them not being left alone, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Victim support worker
23%
AI can already do 23% of this job.
#313 / 633
View rankingVictim support worker
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 |
|---|---|---|---|
Reception, file and the map of services.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Go with them to give evidence, the doctor and court.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Coordinate police, prosecution and services.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Reports, aid and the pathway.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
The crisis, the call and not invading.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Not left alone, and the secrecy of what they tell.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- receive victims
- accompany to evidence
- coordinate with prosecutors
- victim aid
- crisis
- protection pathway
- court with the victim
- professional secrecy
Sources for this job· O*NET 21-1093.00· ESCO/ISCO 2635
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