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The Illusion of Control: Placebo Effects of Control Settings

Open access
Vaccaro, Kristen, Huang, Dylan, Eslami, Motahhare, Sandvig, Christian, Hamilton, Kevin, Karahalios, Karrie·2018·Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3173574.3173590
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Abstract

Algorithmic prioritization is a growing focus for social media users. Control settings are one way for users to adjust the prioritization of their news feeds, but they prioritize feed content in a way that can be difficult to judge objectively. In this work, we study how users engage with difficult-to-validate controls. Via two paired studies using an experimental system -- one interview and one online study -- we found that control settings functioned as placebos. Viewers felt more satisfied with their feed when controls were present, whether they worked or not. We also examine how people engage in sensemaking around control settings, finding that users often take responsibility for violated expectations -- for both real and randomly functioning controls. Finally, we studied how users controlled their social media feeds in the wild. The use of existing social media controls had little impact on user's satisfaction with the feed; instead, users often turned to improvised solutions, like scrolling quickly, to see what they want.

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  • Recent studies have found that usability and user experience do not depend on the quality of personalization and adaptation for fine-tuned AI systems.

    “recent studies have found that usability and user experience do not depend on the quality of personalization and adaptation for fine-tuned AI systems [103].”

    From The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors (2024) · cites this paper as [103]

    The Interview study results and Online study results (page numbers unavailable) found no significant difference in satisfaction with Twitter feeds using real versus random controls. That supports a narrower point about satisfaction with these feed controls, not the general claim that usability and user experience are independent of personalization quality in fine-tuned AI systems. (LLM, conf=0.87)

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1 of the 69 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. [34]34. Zachary C Lipton. 2016. The mythos of model interpretability. arXiv preprint arXiv:1606.03490 (2016). · arXiv
  2. [44]44. Frank A Pasquale. 2011. Restoring transparency to automated authority. https://ssrn.com/abstract=1762766, Journal on Telecommunications and High Technology Law (February 2011).

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Authors
Vaccaro, K., Huang, D., Eslami, M., Sandvig, C., Hamilton, K., & Karahalios, K.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • University of Michigan (US)
ORCID
Venue
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2018-04-19
Pages
1-13
DOI
10.1145/3173574.3173590
OpenAlex
W2795737619
Cited by
65
References
69
Citations by year
2026: 8 · 2025: 3 · 2024: 9 · 2023: 16 · 2022: 6 · 2021: 5 · 2020: 8 · 2019: 6 · 2018: 4
FWCI
4.64
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor, 365 d)
Locations
CHI Conference on Human Factors in Computing Systems (publishedVersion)
Funders
NSF (CHS-1564041)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords
placebo effect, user satisfaction, sensemaking, user expectations, user behavior

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