Case context: A logistics company built a machine learning model to decide which packages should be sent to extra inspection. The model now makes fewer mistakes than the experienced staff who used to do the review by hand. After reaching that point, the team notices that each new round of improvement produces smaller gains.

Question: Using the idea from the source, explain why progress has slowed and what that means for the methods the team can still use.

Sample answer: Progress has slowed because the model is already outperforming the human reviewers. Once that happens, people can no longer easily point to many cases where the model is obviously wrong, so methods that depend on comparing the system with human judgment become less useful. The team can still improve the model, but fewer human-based comparison strategies remain, so advancement is usually slower than it was earlier.

Key points:

• Package screening is an example of a task where the model can move beyond human performance
• Improvement often slows after the system overtakes human reviewers
• When the model is already better, humans have a harder time finding clear errors
• Only some techniques that rely on human comparison still work well

Rubric: Full credit identifies that outperforming human reviewers is the reason progress has slowed, uses package screening as the example domain, and explains that fewer human-comparison methods remain available.