Algorithmic Bias occurs when artificial intelligence or machine learning systems produce systematically skewed outcomes, leading to unfair advantages or disadvantages for certain people or groups. It can arise from biased training data that misrepresents reality, from design choices that embed hidden assumptions, or from feedback loops that amplify existing inequalities. Because an AI model often learns patterns directly from its environment or datasets, any inaccuracies, omissions, or prejudices within those sources can become ingrained and perpetuated.
This issue is central to AI ethics and law because it can cause discrimination and undermine public trust in technology. Left unaddressed, algorithmic bias may reinforce harmful social dynamics or violate human rights by unfairly restricting opportunities and resources. Although AI systems themselves lack moral judgment, humans and organizations do not. They are responsible for building mechanisms to detect, reduce, and prevent bias, such as diversifying training data, refining algorithmic designs, and adopting transparent oversight procedures. Combatting algorithmic bias also calls for awareness of broader social and cultural factors, ensuring that technology respects the dignity and equality of all individuals rather than replicating existing injustices.
