Question 863 of 754
Common Causes of AI Bias
Which THREE factors are common causes of bias in AI systems?
Quick Answer
The answer is biased historical data used for training, along with a lack of diversity in the development team and flawed feature selection or labeling. Biased historical data is the most common cause because AI models learn patterns directly from past data; if that data reflects societal prejudices, underrepresentation, or systemic discrimination, the model will replicate and even amplify those biases. On the CompTIA AI+ AI0-001 exam, this concept tests your understanding that bias is not just a data problem but also a human and design problem—trap answers often focus solely on algorithmic errors while ignoring team composition or data sourcing. A common memory tip is to remember the three D’s: Data (historical bias), Diversity (team homogeneity), and Design (feature/label bias).
⚠ Common exam trap
CompTIA often tests the distinction between statistical bias (e.g., from regularization or validation techniques) and harmful societal bias that leads to unfair outcomes, so candidates mistakenly select options like cross-validation or high regularization as causes of bias.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Lack of diversity in the development team
A lack of diversity in the development team leads to homogeneity of thought, which can cause blind spots in identifying potential biases in data, features, or model behavior. When the team does not represent the full spectrum of end users, the AI system may inadvertently encode assumptions that disadvantage underrepresented groups, resulting in biased outcomes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cross-validation
Why it's wrong here
Cross-validation is a method to evaluate models, not a cause of bias.
- ✓
Lack of diversity in the development team
Why this is correct
Homogeneous teams may overlook biased assumptions.
- ✓
Unrepresentative training sample
Why this is correct
If sample doesn't reflect population, model will be biased.
- ✓
Biased historical data used for training
Why this is correct
Historical biases can be learned by the model.
- ✗
High regularization
Why it's wrong here
Regularization reduces variance, not bias.
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Same concept, more angles
2 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An AI model is being developed for medical diagnosis from X-ray images. The dataset contains only frontal chest X-rays. The model achieves high accuracy on test set but fails on lateral views. What is the most likely cause?
medium- ✓ A.Dataset bias
- B.Underfitting
- C.Label noise
- D.Overfitting
Why A: The model was trained exclusively on frontal chest X-rays, so it never learned features specific to lateral views. When tested on lateral views, the distribution shift causes poor performance, which is a classic case of dataset bias (sampling bias). The high accuracy on the test set is misleading because the test set also only contained frontal views, masking the model's inability to generalize to other X-ray orientations.
Variation 2. An AI system is being developed to diagnose diseases from medical images. The model achieves 99% accuracy on the test set, but when deployed in a different hospital, performance drops significantly. Which of the following is the MOST likely cause?
hard- A.The model is being attacked by adversarial examples.
- ✓ B.The training data does not represent the new hospital's population or imaging equipment.
- C.The model is overfitted to the training data.
- D.Data leakage occurred during preprocessing.
Why B: The model's high accuracy on the test set but poor performance in a different hospital indicates a distribution shift between the training data and the deployment environment. This is a classic case of dataset shift, where the training data does not represent the new hospital's patient population or imaging equipment, leading to degraded model generalization.
Last reviewed: Jun 30, 2026
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