Biases in AI

AI biases can originate from training data, data collection methods, human decisions, model design, or the way an AI system is deployed. For example, historical data may contain existing social biases that a model learns and reproduces. Bias can also emerge when certain populations are underrepresented in training datasets or when inappropriate features influence model decisions.

AI systems increasingly influence decisions in areas such as employment, finance, healthcare, and education. Unaddressed biases can create unfair outcomes, reinforce existing inequalities, and expose organizations to reputational, ethical, and regulatory risks. Identifying and mitigating bias is therefore an important part of responsible AI development.