labelperson

Labelperson is a term that appears in informal discussions and niche texts to describe two related ideas: the human who assigns labels in data labeling tasks, and the social practice of labeling people with attributes or categories. It is not a standard term in formal disciplinary literature.

In data annotation, a labeler or annotator is the person who assigns predefined labels to data samples, such as images, text, or audio, to prepare datasets for machine learning. In casual usage, "labelperson" may be used to refer to the worker performing labeling tasks. Modern labeling efforts often involve multiple labelers to improve accuracy and to measure inter-annotator agreement, with protocols for quality control and dispute resolution.

Separately, the phrase label person can describe how individuals are categorized by others through social labels—such as demographic, occupational, or personality attributes. This usage highlights how labeling can influence perception, identity, and treatment, and it underscores the social dimensions of categorization, including potential biases and stereotypes.

Ethical considerations surrounding labelperson include consent, privacy, representation, and bias. Labeling decisions can affect outcomes for individuals if labels are inaccurate or stigmatizing. Best practices emphasize transparent methodologies, documented labeling guidelines, evaluation of annotation quality, and safeguards against harmful or reductive categorization.

See also: data labeling, annotation, inter-annotator agreement, bias in machine learning, identity labeling.