Detailed Answer in Step-by-Step Solution:

Objective: Assess if bias is a common issue in data science.

Define Bias: Systematic errors in data/models (e.g., skewed training data). Evaluate Statement:

Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely

recognized in ML.

Examples: Gender bias in hiring models, racial bias in facial recognition. Reasoning: Literature and

practice (e.g., fairness in AI) confirm bias as prevalent. Conclusion: A (True) is correct.

OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced

datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns

with industry standards—bias is a well-documented issue, making A true.

: Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".