Why do error analysis processes work best when an ML system automates a human-solvable task?

When an ML system automates a task humans can do well, human-level performance can serve as a benchmark for error analysis.

Working on human-solvable problems provides more powerful _____ tools, enabling more efficient team prioritization.

Match each task characteristic to its implication for applying error analysis procedures.

Order the steps for applying human-level benchmarking in error analysis for a human-solvable ML task.

An ML team builds a system performing a task no human expert can reliably evaluate. What should the team expect about error analysis?

All standard error analysis procedures in Machine Learning Yearning apply equally regardless of whether humans can perform the task.

If an ML system's final output or intermediate components do things _____ cannot do well, some error analysis procedures will not apply.

Match each ML development scenario to its consequence for error analysis tooling.

Order the reasoning steps explaining why human-solvable problems lead to more efficient team prioritization in ML development.