Question

State True or False:
Human Biases in selecting test data can adversely impact the testing phase.

Hint:

Tip: Always use diverse, random, and representative test data to reduce bias and improve fairness.

Answer 1

Bias in test data selection critically impacts AI/ML project outcomes. When humans introduce bias, test data may not accurately reflect real-world populations or scenarios. Consequently, models might excel on biased data but falter in diverse real-world applications. Bias also distorts performance measurements, leading to erroneous conclusions. An unbiased, representative test set is vital for fair evaluation of model generalization and robustness. Thus, the assertion is True: human bias in test data selection demonstrably compromises the testing process.