Fairness in AI systems means ensuring that algorithmic decisions do not create discriminatory or unjust impacts on individuals or groups. This principle is fundamental to building trust and ensuring equitable outcomes.
Key aspects of fairness include:
- Identifying and mitigating bias in training data
- Ensuring representative datasets across different demographics
- Regular auditing of AI system outcomes for disparate impact
- Implementing fairness metrics and monitoring
- Transparent documentation of known limitations
Organizations must actively work to identify potential sources of bias and implement mitigation strategies throughout the AI lifecycle, from data collection to model deployment and monitoring.