What Bias (in AI) Meaning, Applications & Example
The tendency of a model to favor certain outcomes unfairly.
What is Bias in AI?
Bias in AI refers to systematic errors in machine learning models that result in unfair or skewed predictions, often due to imbalanced or unrepresentative training data. Bias can lead to discrimination in areas like hiring, lending, and law enforcement.
Types of Bias in AI
- Selection Bias: Occurs when the training data doesn’t accurately represent the population, affecting model generalization.
- Confirmation Bias: Happens when the model reinforces existing beliefs or patterns in the data, leading to one-sided predictions.
- Algorithmic Bias: Arises from the way an algorithm is designed or trained, causing unfair treatment of certain groups.
Applications and Challenges of Bias in AI
- Hiring and Recruitment: Biased AI can unfairly favor certain candidates, leading to discrimination in hiring.
- Healthcare: Biased models may underrepresent minorities, impacting diagnoses and treatment options.
- Finance: AI in credit scoring may unfairly disadvantage certain demographics due to historical data patterns.
Example of Bias in AI
An example of Bias in AI is facial recognition systems that struggle to accurately identify individuals from underrepresented groups, often resulting in higher error rates for certain ethnicities.