The Illusion of Control: Placebo Effects of Control Settings

By Kristen Vaccaro, Dylan Huang, Motahhare Eslami, Christian Sandvig, Kevin Hamilton, K. G. Karahalios. 2018

73 citations recorded by the source corpus.

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.

Subfields: Safety Research, Sociology and Political Science, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety Research

1. Jennifer Wortman Vaughan
2. S. Whittaker
3. Hanna M. Wallach
4. Daniel G. Goldstein
5. Aaron Springer
6. Q. Liao

In Sociology and Political Science

1. Forough Poursabzi-Sangdeh
2. S. Whittaker
3. Paul N. Bennett
4. Saleema Amershi
5. Hanna M. Wallach
6. Daniel G. Goldstein
7. Rafal Kocielnik
8. Jennifer Wortman Vaughan

In Human-Computer Interaction

1. S. Whittaker
2. Paul N. Bennett
3. Saleema Amershi
4. Hanna M. Wallach
5. Rafal Kocielnik
6. Aaron Springer

Citations in the corpus

• Manipulating and Measuring Model Interpretability (2018, background): “[104] Kristen Vaccaro, Dylan Huang, Motahhare Eslami, Christian Sandvig, Kevin Hamilton, and Karrie Karahalios.”
• Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems (2019, methodology): “Subjective perception of control over the system’s behavior was measured by a question adapted from [47]: “I feel like I have some control over the Scheduling Assistant’s behavior” .”
• Progressive Disclosure: Designing for Effective Transparency (2018, background): “Similarly, giving users placebo controls over an algorithmic interface shows corroborating results [77]; users with placebo controls felt more satisfied with their newsfeed.”