Bias-Variance Tradeoff
Ethics & Safety
Systematic errors in AI systems that result in unfair treatment of certain groups or individuals.
Detailed Explanation
Bias in AI refers to systematic errors that cause AI systems to produce unfair, prejudiced, or discriminatory outcomes against specific groups or individuals. These errors often stem from biased training data, algorithms, or design choices, leading to ethical concerns about fairness, accountability, and social impact. Addressing AI bias is essential for developing equitable and trustworthy artificial intelligence systems.
Use Cases
- •Use case: Detecting and mitigating bias in hiring algorithms to ensure fair candidate evaluation and prevent discrimination.
Related Terms
Other terms in the Ethics & Safety category