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The democratic ethics of artificially intelligent polling
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NETL
NSTL
Springer Nature
This paper examines the democratic ethics of artificially intelligent polls. Driven by machine learning, AI electoral polls have the potential to generate predictions with an unprecedented level of granularity. We argue that their predictive power is potentially desirable for electoral democracy. We do so by critically engaging with four objections: (1) the privacy objection, which focuses on the potential harm of the collection, storage, and publication of granular data about voting preferences; (2) the autonomy objection, which argues that polls are an obstacle to independently formed judgments; (3) the tactical voting objection, which argues that voting strategically on the basis of polls is troublesome; and finally (4) the manipulation objection, according to which malicious actors could systematically bias predictions to alter voting behaviours.
NETL
NSTL
Springer Nature
