Tesary Lin

dblp:324/8990 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0002-1243-6149ORCID · corroborated

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Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Theory of computation · 2 · 1 first-author · 2 since 2021
YearPublicationVenuePosition
2025 Designing Consent: Choice Architecture and Consumer Welfare in Data Sharing
abstract
Regulations mandating consumer consent for data collection and use have led firms to employ "dark patterns"—interface designs that encourage data sharing. We study the causal effects of these designs on consumer consent choices and explore their implications for consumer welfare. We conduct a field experiment where a browser extension randomizes consent interfaces while study participants browse the internet. We find that consumers accept all cookies over half of the time absent dark patterns, with substantial user heterogeneity. Hiding options behind an extra click significantly sways choices toward visible options, while purely visual manipulations have smaller impacts. Users also frequently close banners without active selection, highlighting the importance of default settings. While better-known firms achieve higher baseline consent rates, dark patterns do not amplify this advantage. We use a structural model to show that the consumer surplus-maximizing banner removes dark patterns and defaults to accepting cookies upon consumer inaction. A browser-level global consent choice further improves consumer welfare compared to site-by-site consent interactions, primarily by reducing time costs.
Chiara Farronato, Andrey Fradkin, Tesary Lin
EC3
2023 Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data
abstract
Companies often deploy some form of "choice architecture" when collecting consumer data, designed to nudge consumers towards sharing more private information. This study examines when an emphasis on maximizing the volume of data shared when deploying choice architecture can alter the composition of the collected data, hence creating a trade-off between the quantity and representativeness of data collected. To this end, we ran a large-scale choice experiment to elicit consumers' incentive-compatible valuation for their private Facebook data while randomizing the choice frames they encountered. Within participants, we elicited WTA using a multiple-price list, followed by a free-text entry. Across participants, we randomized the choice default and the price anchor. The default varied between opt-in, opt-out, and active choice. Price anchor was the range of prices in the multiple price list, which was either $0--$50 (low) or $50--$100 (high).
Tesary Lin, Avner Strulov-Shlain
EC1