Algorithmic Bias

Algorithmic Bias refers to systematic and repeatable patterns in a Computer System that produce unfair outcomes, such as unjustifiably privileging or disadvantaging particular groups. It is a sociotechnical phenomenon arising from interacting sources, including historical prejudices and Sampling Bias in Training Data, Measurement Bias, flawed Algorithm design, inappropriate Feature Selection, Evaluation Bias, Feedback Loops, deployment context, and systemic or institutional inequalities. These influences can shape a Machine Learning system throughout its development and use, rather than originating solely in its data or model design. In Deep Learning and Large Language Models, manifestations include stereotyped or targeted toxic outputs and unfair disparities in performance across demographic groups. Some major AI companies, including Google DeepMind and Microsoft, publish Responsible AI and Algorithmic Fairness research, including work on Bias Mitigation and Algorithmic Auditing. Addressing algorithmic bias requires examining both technical choices and the social settings in which systems operate; failure to do so can cause significant real-world harm in high-stakes applications such as Predictive Policing, Automated Hiring, and Credit Scoring.