Understanding data as cyclical rather than extractive: platforms must return value to communities generating data rather than capturing it unidirectionally.
The Tao Te Ching emphasizes return and circulation: what goes out must return; cycles complete themselves. Data extraction in algorithmic politics violates this principle—users generate valuable information (behavioral data, attention, relationships) that platforms capture, monetize, and redirect without returning proportional value. This creates systemic imbalance: communities become depleted while platforms concentrate wealth and power. A return-circulation framework reconceives data governance as reciprocal exchange. Users who generate data should share in value creation; algorithmic insights should feed back to communities that created them; platforms should circulate benefits rather than concentrate them. This requires rethinking data ownership: instead of treating data as extractable resources, recognize it as relational—existing only through community participation. Historical precedent appears in commons management systems where resource use rights return value to stewards. Applied to algorithmic politics, circulation means profit-sharing models, community-controlled data repositories, and governance structures where data contributors shape algorithmic decisions affecting them.
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