Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral Advertising

要旨

Tech companies that rely on ads for business argue that users have control over their data via ad privacy settings. However, these ad settings are often hidden. This work aims to inform the design of findable ad controls and study their impact on users’ behavior and sentiment. We iteratively designed ad control interfaces that varied in the setting's (1) entry point (within ads, at the feed’s top) and (2) level of actionability, with high actionability directly surfacing links to specific advertisement settings, and low actionability pointing to general settings pages (which is reminiscent of companies' current approach to ad controls). We built a Chrome extension that augments Facebook with our experimental ad control interfaces and conducted a between-subjects online experiment with 110 participants. Results showed that entry points within ads or at the feed’s top, and high actionability interfaces, both increased Facebook ad settings’ findability and discoverability, as well as participants' perceived usability of them. High actionability also reduced users' effort in finding ad settings. Participants perceived high and low actionability as equally usable, which shows it is possible to design more actionable ad controls without overwhelming users. We conclude by emphasizing the importance of regulation to provide specific and research-informed requirements to companies on how to design usable ad controls.

著者
Jane Im
University of Michigan, Ann Arbor, Michigan, United States
Ruiyi Wang
University of Michigan, Ann Arbor, Michigan, United States
Weikun Lyu
University of Michigan, Ann Arbor, Michigan, United States
Nick Cook
University of Michigan, Ann Arbor, Michigan, United States
Hana Habib
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Lorrie Faith. Cranor
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Nikola Banovic
University of Michigan, Ann Arbor, Michigan, United States
Florian Schaub
University of Michigan, Ann Arbor, Michigan, United States
論文URL

https://doi.org/10.1145/3544548.3580773

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Social Media and Information

Room Y03+Y04
6 件の発表
2023-04-25 23:30:00
2023-04-26 00:55:00