Signed Exposure: Fair Routing of Algorithmic Attention When Attention Can Harm
Fairness-of-exposure treats algorithmic attention as a good to be distributed equitably. But when an autonomous agent initiates contact, attention is signed: it delivers value to a willing receiver and imposes a burden on an unwilling one. We formalize routing under signed exposure and show that a fair distribution of attention need not be a fair distribution of unwanted attention. Our central result is an incompatibility: within signed-exposure routing, exposure parity (equal contact rates across groups) and burden parity (equal unwanted-contact rates) generically cannot hold at once, and the two are separated by a band that widens as routing grows more selective. A second result shows measurement error is itself a fairness mechanism: group-differential noise in receptivity scores simultaneously inflates a group's exposure and degrades whom it selects, so an apparent exposure-fairness gain is a hidden burden transfer. Calibrating to a public dating-platform survey (n=2,499) that, to our knowledge, uniquely measures receive-side receptivity to conversational agents, we find exposure parity costs only 0.2--2.3% of yield yet moves the per-capita burden ratio to 1.7 times: the tension is between fairness notions, not between fairness and efficiency. Finally, the burden-parity policy is computable by bisection and learnable online: a plug-in learner recovers it at a $2.3\%$ empirical regret premium. The operative design choice in signed-exposure markets is not efficiency versus fairness but which fairness.