Automation index

How much of a Crime scene technician’s job can AI already do?

A trace protocol comes earlier. Not contaminating the scene so it holds up in court do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

IS WORK OVER?

23%

AI can already do 23% of this job.

#315 / 633

Crime scene technician

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
Inspection, photos and the scene sketch. Estimate No public source provides a precise figure. This is our estimate.	16%	36%	+5.8
Collect traces, prints and the DNA that gets walked on. Estimate No public source provides a precise figure. This is our estimate.	18%	12%	+2.2
Chain of custody, labels and the lab. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Report, reconstruction and the file. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
The dirty scene, the rush and the one who enters ungloved. Estimate No public source provides a precise figure. This is our estimate.	18%	18%	+3.2
That it holds in court, and not breaking the chain. Estimate No 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

IS WORK OVER?

29%

#64 / 633

ML engineer

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. Estimate No public source provides a precise figure. This is our estimate.	22%	48%	+10.6
Evaluate, document and compare runs. Estimate No public source provides a precise figure. This is our estimate.	18%	42%	+7.6
Deploy, monitor and retrain. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Talk to product about what a good error is. Estimate No public source provides a precise figure. This is our estimate.	16%	15%	+2.4
Dirty data, bias and model limits. Estimate No public source provides a precise figure. This is our estimate.	14%	18%	+2.5
On-call, incidents and rollback. Estimate No 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