A company wants to deploy a machine learning model for hiring. They've ensured that the model is highly accurate. However, they're facing criticism because the inner workings of their model are a "black box," and candidates want to know why they were or were not selected. This criticism mainly pertains to which aspect of machine learning?
- Explainability
- Accuracy
- Training Data
- Hyperparameter Tuning
The criticism about the model being a "black box" highlights the need for explainability in machine learning. It's essential to understand how and why the model made hiring decisions, not just the accuracy of those decisions.
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