What is AI Bias?

Unfair Outcomes: When AI Reflects and Amplifies Prejudices

Sources of Bias in AI Systems: Data and Algorithms

Strategies for Building Equitable and Unbiased AI

**AI Bias** refers to **systematic and repeatable errors or unfair outcomes in an AI system that arise from prejudiced assumptions or imperfections in the training data or algorithmic design**. This can lead to discriminatory results against certain groups, reinforce stereotypes, or perpetuate existing societal inequalities. For example, an AI hiring tool might unfairly disadvantage certain demographics if trained on biased historical data. Identifying and mitigating AI bias is a critical challenge in **Responsible AI** and **AI safety**, requiring careful data curation, algorithmic auditing, and continuous monitoring to ensure fair and equitable AI systems in 2025. ⚖️🚫