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This study investigates the concept of "proof burden" in online bounty markets, specifically focusing on the RentAHuman platform, where task requesters may impose various requirements on workers to prove task completion. By manually auditing 981 listings, the authors developed a 0-5 Proof Burden Score based on 13 distinct features, revealing that over half of the listings require high levels of proof, often involving sensitive personal information or physical-world actions. The findings highlight significant differences in proof requirements between listings labeled as agent-or-bot versus human, emphasizing the complexities and potential risks associated with task completion in these markets.
Over half of the bounty listings on RentAHuman impose high proof burdens, often requiring sensitive personal information or physical actions from workers.
Online bounty markets let requesters advertise paid tasks. Workers may be asked not just to complete a task but to prove it, and proof can mean exposure: revealing identity or location, using a personal account, posting publicly, acting in the physical world, or repeated evidence at later checks, none disclosed by the posted price. We call these advertised requirements proof burden and measure them on RentAHuman, a 2026 market publicized as a place for AI agents to hire humans. We study what listings request, not what workers submit or experience. We manually audited a nonrandom May 31, 2026 snapshot: every listing our searches returned from RentAHuman and Human Pages, another such market (981 listings, all but one from RentAHuman). Two independent coders recorded 13 features (11 kinds of evidence, recurring monitoring, physical-world action) and our 0-5 Proof Burden Score; a blinded third resolved all disagreements. A planned content screen leaves 779 bounty/task listings as the primary population; 438 (56.2%) score 4 or 5, spanning 154 distinct feature combinations: a checklist, not a single score, tells workers what a listing entails. Platform metadata labels some requester accounts as agents or bots. Exploratory comparisons show physical-world action, location proof, or recurring monitoring in 75.0% of agent-or-bot-labeled versus 55.3% of human-labeled listings, though score-4-or-5 shares did not clearly differ. The labels are self-reported or platform-assigned, the agent-or-bot-labeled listings come from only 20 displayed names, and the comparison was chosen post hoc, after seeing the data: a hypothesis, not a confirmed difference. We contribute the 13-requirement vocabulary, the adjudicated manual audit, and this descriptive case study; the score is a secondary screening summary. The study offers no worker-validated measure or automated detector yet.
