Paper accepted at CHI ’23.

Understanding Dark Patterns in Home IoT Devices

Monica Kowalcyzk (Northeastern University), Johanna Gunawan (Northeastern University), David Choffnes (Northeastern University), Daniel J. Dubois (Northeastern University), Woodrow Hartzog (Boston University), Christo Wilson (Northeastern University)

Last updated: 01/13/2023

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.

ABOUT THIS PUBLICATION

Paper title: Understanding Dark Patterns in Home IoT Devices

Authors: Monica Kowalcyzk (Northeastern University), Johanna Gunawan (Northeastern University), David Choffnes (Northeastern University), Daniel J. Dubois (Northeastern University), Woodrow Hartzog (Boston University), Christo Wilson (Northeastern University)

Full Text (PDF): available.

Citation:

@inproceedings{gunawan-2023-chi,
  author = {Monica Kowalcyzk and Johanna Gunawan and David Choffnes and Daniel Dubois and Woodrow Hartzog and Christo Wilson},
  title = {{Understanding Dark Patterns in Home IoT Devices}},
  booktitle = {{Proceedings of ACM Human Factors in Computing Systems (CHI 2023)}},
  address = {Hamburg, Germany},
  month = {April},
  year = {2023},
}

ACKNOWLEDGMENTS

  • This research was partially supported by:
    • Consumer Reports Digital Lab
    • Google Aspire award
    • NSF (ProperData SaTC-1955227, CNS-1900879)