developer-roadmap/src/data/roadmaps/ai-engineer/content/bias-and-fairness@lhIU0ulpvDAn1Xc3ooYz_.md

Bias and Fairness

Bias and fairness in AI refer to the challenges of ensuring that machine learning models do not produce discriminatory or skewed outcomes. Bias can arise from imbalanced training data, flawed assumptions, or biased algorithms, leading to unfair treatment of certain groups based on race, gender, or other factors. Fairness aims to address these issues by developing techniques to detect, mitigate, and prevent biases in AI systems. Ensuring fairness involves improving data diversity, applying fairness constraints during model training, and continuously monitoring models in production to avoid unintended consequences, promoting ethical and equitable AI use.

Learn more from the following resources:

• @article@What Do We Do About the Biases in AI?
• @article@AI Bias - What Is It and How to Avoid It?
• @article@What about fairness, bias and discrimination?