Systematic errors in an algorithm or AI model that produce unfair outcomes for certain people or groups.
Algorithmic bias often comes from the data a model learns from. If historical data reflects unequal treatment, or under-represents some groups, the model can repeat or amplify those patterns. Bias can also come from how a problem is defined, which features are used, or how results are measured.
A frequently cited example is a hiring tool trained on past successful applicants. If most past hires came from one background, the tool may learn to rank similar candidates higher and screen others out unfairly. Detecting bias involves testing model performance across different groups, reviewing training data, and monitoring outcomes after deployment. Reducing it may require rebalancing data, changing model design, or keeping humans responsible for high-stakes decisions such as lending, hiring, and healthcare.
The policies, roles, and processes an organization uses to decide how AI is built, bought, used, and monitored responsibly.
Explainable AIMethods and tools that help people understand why an AI model produced a particular prediction or decision.
Machine LearningA branch of AI in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each case.
A plain-English map of the state AI laws in effect or enacted as of October 2026, from Colorado's rewritten AI Act to chatbot, hiring and deepfake rules.
A buyer's checklist for AI meeting assistants covering recording consent laws, accuracy testing, integrations, data retention, security and cost.
A worked AI ROI formula, the baseline metrics to capture before a pilot starts, and the hidden costs that make AI business cases look better than they are.