Definition
Human-centered AI
An approach to building AI that puts people's needs, values, and wellbeing first throughout design and deployment.
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Related terms in AI Fundamentals
GuardrailsRules built into an AI product to detect risk and route users to proper help.HallucinationWhen an AI model produces information that is false or made up but presents it as fact.Hallucination ToleranceThe acceptable error rate for AI outputs in a given system context. In consumer chatbots, high hallucination tolerance is acceptable — wrong answers are annoying but not dangerous. In healthcare eligibility determinations, medication routing, or financial compliance, the tolerance is zero — errors create legal liability, financial harm, or patient safety risks. Designing for zero hallucination tolerance requires architectural decisions (human-in-the-loop checkpoints, output traceability, auditability standards) rather than simply selecting better models. Softheon's target: 99.9999999% accuracy — exceeding air traffic control standards.Hallucination-Free AIAn AI system constrained to answer only from a specified data set, with no access to the broader internet or general training data. Eliminates invented or inaccurate responses by removing the source of uncertainty — the model can only cite what actually exists in the collection.Humane AIAI designed to strengthen human connection and meaning rather than maximize engagement.Hybrid AIDeliberately splitting workloads across frontier models, self-hosted open source models, and fully sealed environments.HyperparameterA setting chosen before training begins, like learning rate or model size, that shapes how a model learns.InferenceWhen a trained model is put to work on new inputs, making predictions or generating answers. It is what happens each time you use an AI product.