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Comment by ramraj07 | Hacker News Reader

That’s a long paper to write given it’s all based on a survey of 50 people.

Nevertheless the point is true, and importantly it’s probably caused by “AI engineers” who are actually pretty mediocre and were just attracted to this job for the wrong incentives. If interviews for DS and AI focus on recruiting people for not just theoretical middling knowledge but also the drive to solve “the real problem” that will let them tackle the project in a truly fundamental way, the majority of issues pointed in this paper will probably go away.

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This is a qualitative, not quantitative study. The goal of qualitative research (in cases like this) is, essentially, finding categories, not the relative distribution of the population in those categories. I've seen well-executed qualitative studies with less than 10 participants, 53 is actually on the higher end for this approach.

You usually try to select for a pretty diverse cohort (which the authors did in this case), not a representative one.

A diverse cohort means you'll probably have all categories that matter. A representative cohort means you'll have a distribution over these categories that is pretty similar to the real world.

The result here is essentially displayed in Figure 1. One could now follow up with a quantitative design to e.g. discern which particular step is the most common obstacle.

I wanted to point out the same thing, I'm counting six authors on this piece of science complaining there aren't any doers around. At this point we have reached peak papers, there is more truth to be found in a HN thread and thats saying a lot.

Agree, it lost some credibility for me as well. I immediately search for methodology before reading anything nowadays.

> We presented a qualitative study of data practices and challenges among 53 AI practitioners in India, East and West African countries, and the US, working on cutting-edge, high-stakes...

I'd rather read a well-written opinion article and hear more about what those 53 people have to say.

Are you saying that; quantities studies > qualitative studies?

Or are you saying that their qualitative methodology is lacking? Is so, what would be a better way of approaching a topic like this?

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