Why Data Scientists Are Still Doing the Hard Part After AI Automation??

Read time: 2.5 minutes

Data scientists learn these 5 things quickly:

1️⃣ AI is like an entry-level employee.

Quick, confident, often wrong.
Solution: View AI output as draft documents instead of final judgments.

2️⃣ Review time increases but Build time decreases.

The output speed has increased; however, overall effort hasn’t decreased.
Solution: Track the amount of reviewer time versus pure model output.

3️⃣ You still have to be responsible for what the model produces.

You're responsible for the results from using AI technology.
Solution: Clearly identify who is responsible for reviewing each AI-based model.

4️⃣ Errors may be more difficult to detect, but not necessarily fewer in number.

AI systems now produce erroneous outputs with multiple inferred details rather than obvious errors.
Solution: Focus on improving validation processes rather than increasing your confidence level.

5️⃣ Senior time is consumed first.

In an AI-based environment, experience is critical where AI does not work.
Solution: Factor the costs of the AI versus the costs of an experienced reviewer.

💡Key Takeaway: 

The role of a data scientist has been transformed by AI; instead of spending time developing solutions from the ground up, Data Scientists spend more time examining, verifying and defending results produced by machines. The promise was never to replace people. It was to create a way to work faster while taking on increased responsibility.