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Terms and conditions (T&Cs) are pervasive on the web and often containimportant information for consumers, but are rarely read. Previous research hasexplored methods to surface alarming privacy policies using manual labelers,natural language processing, and deep learning techniques. However, this priorwork used pre-determined categories for annotations, and did not investigatewhat consumers really deem as important from their perspective. In this paper,we instead combine crowdsourcing with an open definition of "what is important"in T&Cs. We present a workflow consisting of pairwise comparisons, agreementvalidation, and Bradley-Terry rank modeling, to effectively establish rankingsof T&C statements from non-expert crowdworkers on this open definition, andfurther analyzed consumers' preferences. We applied this workflow to 1,551 T&Cstatements from 27 e-commerce websites, contributed by 3,462 unique crowdworkers doing 203,068 pairwise comparisons, and conducted thematic andreadability analysis on the statements considered as important/unimportant. Wefound that consumers especially cared about policies related to after-sales andmoney, and tended to regard harder-to-understand statements as more important.We also present machine learning models to identify T&C clauses that consumersconsidered important, achieving at best a 92.7% balanced accuracy, 91.6%recall, and 89.2% precision. We foresee using our workflow and model toefficiently and reliably highlight important T&Cs on websites at a large scale,improving consumers' awareness

Identifying Terms and Conditions Important to Consumers using Crowdsourcing