Document Type
Article
Publication Date
4-2023
ISBN
9781450394215
Publisher
Association for Computing Machinery
Language
en-US
Abstract
Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits. Speakers, doorbells, and camera devices contain the most dark patterns, with manufacturers of such devices (Amazon and Google) having the most dark patterns compared to other vendors. We investigate how this distribution impacts the potential for consumer exposure to dark patterns, discuss broader implications for key stakeholders like designers and regulators, and identify opportunities for future dark patterns study.
Recommended Citation
Monica Kowalczyk, Johanna Gunawan, David Choffnes, Daniel J. Dubois, Woodrow Hartzog & Christo Wilson,
Understanding Dark Patterns in Home IoT Devices
,
in
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
1
(2023).
Available at:
https://doi.org/https://doi.org/10.1145/3544548.3581432
Comments
Copyright © 2023 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.