Multiple choice › Bias in AI and ML
Bias in AI and ML
1. Bias in AI and ML
A face-recognition system trained mostly on photos of one demographic group performs poorly for other groups. This is an example of:
- Rounding error
- Race condition
- Cross-site scripting
- Data set source bias
Answer
D
If the training data is not representative, the model learns skewed patterns. Human bias (explicit or implicit) can also enter during design and labelling.
2. Bias in AI and ML
Which action BEST reduces bias in an ML hiring tool?
- Use diverse, representative training data and regularly audit outcomes
- Remove all testing to save time
- Hide how the model works
- Train it only on past hires
Answer
A
Representative data plus ongoing evaluation and diverse development teams reduce harmful bias.