Biases

Biases are systematic tendencies to deviate from objective judgment or from the truth in thinking, perception, and data interpretation. They can arise from cognitive processes, social influences, emotional states, and technological systems. Biases may be unconscious (implicit) or conscious, and they can affect individuals and institutions alike. When biases shape beliefs, decisions, or measurements they can lead to distorted conclusions, unfair outcomes, and reduced reliability of information.

Common cognitive biases include confirmation bias (favoring information that supports preexisting beliefs), anchoring (relying too heavily on an initial value), availability heuristic (overestimating the likelihood of memorable events), hindsight bias, and attribution biases (misjudging causes of behavior). Other biases stem from perception, memory, and social attribution, such as fundamental attribution error and status quo bias. Biases also occur in data collection and analysis, including sampling bias, measurement bias, selection bias, observer bias, and reporting bias.

Biases have wide-ranging consequences in science, medicine, journalism, finance, and policy, from misdiagnoses and flawed experiments to unfair hiring practices and biased algorithms. Recognizing and mitigating biases involves awareness training, structured decision making, debiasing techniques, preregistration, blind reviews, and seeking diverse perspectives. In data and AI contexts, responsible practices include careful data curation, transparency, fairness metrics, external auditing, and ongoing monitoring to reduce algorithmic bias and equity gaps.