Data’s core function is differentiation, which compounds existing social discrimination.

Evidence

The purpose of collecting data is always to differentiate: data’s purpose is to generate information that usefully discriminates between entities. Again, discrimination in itself is not bad: we want a heart recorder that correctly distinguishes between a heartbeat and other bodily signals. The issue is how data discriminations are applied within the framework of existing structures of social discrimination. Today’s vast infrastructures of connection, like all forms of power, build on existing inequality. But they also create new forms of inequality through various means: inequalities of data capital, exploitation of the human labor that sustains Machine Learning, and the ability of data-driven categorizations to reinforce those existing inequalities. Just as the capitalization of work through labor relations has over time produced a highly unequal distribution of the profits generated by labor, so over time we can expect the capitalization of life through data relations to introduce new forms of inequality into human life itself. We can increasingly expect human beings to be socially managed in terms of their data value—that is, their value as data inputs to capitalist production. — Nick Couldry & Ulises A. Mejias, The Costs of Connection, 125

Categorization

• DEEPTHINKING: The analogy between capitalization of work and capitalization of life is a paradigm-level claim that frames data colonialism’s inequality logic as a historical parallel to labor exploitation — this resists one-sentence summary and reframes how the reader evaluates extraction.
• XREF: Parallels the ‘data value as social management’ claim with Marxist labor theory of value; diverges from attention-economy frames that see the harm in distraction rather than in surplus-value extraction from life itself. — connects to: Mere categorization into a group triggers differentiation and discrimination; Privately controlled data extraction produces social classifications that entrench inequality — Nick Couldry & Ulises A. Mejias, The Costs of Connection: How Data Is…, 443; Identification with even meaningless categories can drive discrimination (+3 more)
• ADDENDUM: Directly extends the ‘On Sacrifice’ article’s logic about stratified societies — here the ritualized extraction is continuous and data-driven rather than symbolic, a modern form of ordering human value.

Broader Connections

• Privately controlled data extraction produces social classifications that entrench inequality #corroborates (0.90) — Nick Couldry & Ulises A. Mejias, The Costs of Connection, 443
• Mere categorization into a group triggers differentiation and discrimination #expands (0.80)
• Datafied discrimination is opaque to subjects and operators alike, so uncorrected #expands (0.80) — Nick Couldry & Ulises A. Mejias, The Costs of Connection, 580
• Identification with even meaningless categories can drive discrimination #expands (0.75)
• Social evolution drives differentiation, which in turn generates more generalized communication media. #qualifies (0.75) — Niklas Luhmann, Trust and Power, 583