overinclusive

Overinclusive refers to a criterion, definition, or rule that includes more elements than intended, thereby covering a broader set than the target category. It is the opposite of underinclusive, which excludes items that should be included. The term is used across law, logic, and data analysis to describe a mismatch between the intended scope and the actual scope of a rule or definition.

In law and policy analysis, overinclusiveness occurs when a statute or regulation restricts more conduct or behavior than necessary to achieve a legitimate aim. This can raise concerns about fairness, constitutionality, and effectiveness. For example, a public-safety regulation that bans any activity in a park after sunset may sweep in many noncriminal or benign activities, imposing burdens on ordinary parkgoers. Narrowing the rule to address specific risks or targets reduces overinclusiveness.

In logic, semantics, and set theory, an overinclusive predicate or definition extends beyond the intended domain, including individuals or objects that do not meet the core criterion. This can weaken arguments or symbolic clarity, especially if the misclassification alters conclusions about a concept or category.

In data filtering and machine learning, an overinclusive rule tends to produce many false positives: it captures more instances than are truly relevant. For example, a classifier with high recall but low precision labels many non-target items as positive, reducing discriminative value.

See also: underinclusive, overbreadth, precision, recall.