Automation index
How much of a Equality officer’s job can AI already do?
A stock equality plan comes earlier. Stopping what happens in the corridor do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Equality officer
29%
AI can already do 29% of this job.
#72 / 623
View rankingEquality 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 |
|---|---|---|---|
Diagnosis, equality plan and the pay register.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Harassment protocols, training and clauses.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The complaint, the committee and what nobody wants to hear.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Indicators, annual report and bargaining with the firm.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The agreement, tenders and social clauses.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
That it is done, and not left as a PDF.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
- equality plans
- pay gap register
- harassment protocols
- equality training
- gap diagnosis
- social clauses
- equality indicators
- bargain with the firm
Sources for this job· O*NET 13-1041.00· ESCO/ISCO 2422
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