Case context: A machine learning team is developing an image recognition system for medical diagnostics. The system currently misclassifies 40% of the images. The team must choose between having the dataset labeled by a group of junior doctors who have a 10% error rate, or hiring a much more expensive panel of expert specialists who have a 5% error rate.

Question: Given the current 40% error rate of the medical image recognition system, which group of medical professionals should the team prioritize for labeling data and providing intuitions? Explain your decision based on the principles of human-level error reference.

Sample answer: The team should choose the junior doctors. Because the system's current error rate is very high at 40%, using a junior doctor with a 10% error rate versus an expert with a 5% error rate does not matter much. The gap between 40% and 10% provides more than enough room and sufficient tools to guide the improvement of the algorithm without needing the more precise 5% benchmark.

Key points:

• Diagnose the current system error as high (40%).
• Determine that the difference between 10% and 5% human error references is negligible at this stage.
• Decide that the less precise (junior doctor) reference is sufficient to provide tools for system improvement.

Rubric: A correct response identifies that the junior doctors are sufficient and explains that at a 40% system error rate, the difference between a 10% and 5% human-level reference is practically insignificant for providing intuitions.