selfderived

Selfderived is a term used to describe information, conclusions, or artifacts that are produced from a subject's own data, reasoning, or experiences, rather than being sourced from external inputs. The term is not widely standardized and appears in discussions across philosophy, data science, and human-computer interaction.

The construction derives from self and derived; it is a neologism that has gained some use with the rise of self-supervised learning and introspective reasoning in cognitive science. It is not tied to a single canonical definition.

In philosophy, self-derived knowledge refers to conclusions that arise from an agent’s internal reflection, introspection, or personal experience, but such knowledge may be criticized for lacking external validation.

In data science and AI, self-derived data or features are those produced by a model or system from its internal representations or prior outputs, rather than from external databases. Examples include synthetic data generated from a model’s own predictions or features engineered through self-reflection of representations.

Potential benefits include reduced reliance on external data and improved personalization; risks include circular reasoning, bias amplification, and privacy concerns if self-derived data encodes sensitive traces of user behavior.

See also self-reference, self-supervised learning, circular reasoning, data provenance.