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The growing demand for data and AI-generated digital goods, such aspersonalized written content and artwork, necessitates effective pricing andfeedback mechanisms that account for uncertain utility and costly production.Motivated by these developments, this study presents a novel mechanism designaddressing a general repeated-auction setting where the utility derived from asold good is revealed post-sale. The mechanism's novelty lies in using pairwisecomparisons for eliciting information from the bidder, arguably easier forhumans than assigning a numerical value. Our mechanism chooses allocationsusing an epsilon-greedy strategy and relies on pairwise comparisons betweenrealized utility from allocated goods and an arbitrary value, avoiding thelearning-to-bid problem explored in previous work. We prove this mechanism tobe asymptotically truthful, individually rational, and welfare and revenuemaximizing. The mechanism's relevance is broad, applying to any setting withmade-to-order goods of variable quality. Experimental results on multi-labeltoxicity annotation data, an example of negative utilities, highlight how ourproposed mechanism could enhance social welfare in data auctions. Overall, ourfocus on human factors contributes to the development of more human-aware andefficient mechanism design.

No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctions