Artificial Intelligence
What is AI Bias
Prejudice in AI data and models
AI Bias
AI Bias is a systematic error or prejudice in artificial intelligence systems that leads to unfair or discriminatory outcomes.
Types of Bias
| Type | Description | Example | |------|-------------|---------| | Data bias | Unrepresentative sample | Training on single region data | | Algorithmic | Flaws in model logic | Amplifying existing patterns | | Human | Developer prejudices | Subjective data labeling | | Historical | Perpetuating past injustices | Hiring discrimination |
Consequences
- Discrimination — unfair decisions based on gender, race, age
- Reputational risks — scandals and loss of trust
- Legal issues — violations of equality laws
- Economic losses — suboptimal business decisions
Detection and Mitigation Methods
- Data audit — checking sample representativeness
- Fairness metrics — model fairness measurements
- Adversarial testing — vulnerability assessment
- Diverse teams — diversity in development teams
- Continuous monitoring — ongoing production monitoring