Biased Data
Biased data refers to datasets that contain systematic errors or distortions that lead to unfair, inaccurate, or skewed outcomes when used in analysis or machine learning models. It often arises from sampling issues, measurement errors, or historical prejudices embedded in the data collection process. This concept is critical in fields like data science, AI ethics, and statistics, as it can perpetuate discrimination and reduce model reliability.
Developers should learn about biased data to build fair and robust AI systems, especially when working on applications involving hiring, lending, or criminal justice where bias can have serious societal impacts. Understanding this concept helps in implementing data preprocessing techniques, bias detection tools, and ethical guidelines to mitigate risks and ensure compliance with regulations like GDPR or AI fairness standards.