True or false? Bias is a common problem in data science applications.

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".