Pu (the uncarved block) as concept for pre-categorized data: raw information before algorithmic interpretation reveals biases embedded in classification itself.
The 'uncarved block' (pu) in Taoist thought represents wholeness before fragmentation. Applied to algorithmic politics, this concept suggests examining data before algorithmic categorization. Citizens encounter not raw data but already-carved information: sorted, labeled, interpreted through algorithmic frames. These frames embed political choices—how content is categorized determines what citizens understand as related, relevant, or opposed. The uncarved block perspective asks: what political assumptions exist in the very act of data classification? How do algorithmic categories simplify complex political reality? By examining data at its least-processed state, political algorithms become more transparent about their interpretive choices. This means showing citizens not just algorithmic outputs but the underlying categorizations, inviting critical interrogation of how systems slice reality. It means algorithms that acknowledge their own simplifications—representing the uncarved block's irreducible complexity rather than pretending clean categories capture political truth. This framework makes algorithmic interpretation visible as interpretation, not inevitable reality.
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