Privately controlled data extraction produces social classifications that entrench inequality

Evidence

Today’s social knowledge is produced principally through privately controlled data extraction whose goal is to discriminate between social actors for economic advantage. Algorithms help “fram[e] problems” and provide the classifications on which broader social differentiations are constructed. But “many classifications are now embedded in markets,” a notable example being credit rating systems. Data-driven social classifications have profound consequences for the fairness or otherwise of the social world. Indeed, various entrenched features of the new social knowledge incline it toward enhancing not justice but inequality. — Nick Couldry & Ulises A. Mejias, The Costs of Connection, 443

Categorization

• XREF: Parallels the surveillance/attention school’s claims about algorithmic sorting, but reframes it as data colonialism’s extractive relation — exactly the rival-school mapping the reader wants. — connects to: Social software can promote inequality rather than counter it. — Alice E. Marwick, Status Update: Celebrity, Publicity,…, 19; Digital speculation obscures inequality through hidden code just as nations hid exploitation — Aris Komporozos-Athanasiou, Speculative Communities: Living with…, 138; Seamless data flows breed opaque inequality and uncontrolled surveillance — Nick Couldry & Ulises A. Mejias, The Costs of Connection: How Data Is…, 607 (+14 more)
• DEEPTHINKING: The closing claim is dense — ‘various entrenched features of the new social knowledge incline it toward enhancing not justice but inequality’ compresses a whole structural argument into one sentence.

Broader Connections

• Inequality serves surveillance capitalism as a lever for extracting consent #expands (0.82) — Shoshana Zuboff, The Age of Surveillance Capitalism, 800