Path: Working well on data projects
Do not trade accuracy for throughput
Decide when to flag, when to skip, and when a guess costs the project more than an unfinished item.
- Starting level
- Comfortable with the basics
- Time
- 18 minutes
- Public lesson guide
- EN + हिंदी
You will learn to
Decide when to flag, when to skip, and when slowing down is the correct choice.
Why this matters
A flagged item is honest, finished work. A guessed one looks finished and is trusted later by people who have no reason to check it.
Three things to remember
- Flag the unclear item; never guess to keep the count moving.
- Speed comes from fewer re-reads, not from fewer checks.
- Tell the project what you flagged when you hand the batch in.
See one example
An hour is left in a transcription batch and a handful of lines are genuinely inaudible.
Try the judgement yourself
Both answers respond to the task above. One of them does the job better. Which, and why?
Make something yourself
Write your own working rule for a batch under time pressure: what you flag, what you skip, when you slow down, and what you tell the project at hand-in.
Read further
The source is optional. The complete teaching and practice are available inside AI2Bharat.
Read the primary or official source: Gebru et al., Datasheets for Datasets (arXiv)