A Slow Algorithm Improves Users' Assessments of the Algorithm's Accuracy
Open accessAbstract
With computational algorithms making an increasing number of deeply consequential, and often problematic judgments on our behalf, there is a growing interest in slowing down technology to encourage users to reflect on judgments made by algorithms. Prior work in slow technology has established slowness as an agent of reflection and serendipity; however, it has been unclear whether this waiting time actually helps users gain useful insight or any other benefits as they make judgments using an algorithm. To this end, we conducted a series of online and in-person between-subject user studies in which we isolate the impact of an algorithm's speed on how users incorporate the algorithm's advice when making judgments in the context of simple visual recognition tasks. We find that our participants followed good quality algorithms more and bad quality algorithms somewhat less if the response time of the algorithm is slower. Furthermore, qualitative analysis of the in-person study interviews reveals that the waiting was not time wasted, but was often used to reflect on the task and the estimation process of themselves and the algorithm, and to compare and reevaluate the two processes. Based on these findings, we outline design implications of future algorithmic systems.
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Several mechanisms have been proposed to optimize the combination of human and AI decisions and facilitate appropriate reliance, including interpretable explanations, cognitive forcing functions, and feedback loops.
“These mechanisms include providing interpretable explanations for AI advice [21, 72, 123], using cognitive forcing functions[17, 47, 99], and incorporating feedback loops to enhance the interaction between humans and AI systems [10, 11, 139].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [99]
Referring to AI predictions as suggestions counteracts perfect automation schemas and emphasizes that the suggestion can be rejected.
“We always refer to AI predictions as “suggestions” [74] to counteract perfect automation schemas [21] and emphasize that this suggestion can be rejected.”
From Explanations Can Reduce Overreliance on AI Systems During Decision-Making (2023) · cites this paper as [74]
This is arguably a necessary trade-off within current GenAI systems, as it enables diversity of output, yet it exacerbates the challenge of confidence adjustment, particularly when working iteratively across prompting and output evaluation.
“This is arguably a necessary trade-off within current GenAI systems, as it enables diversity of output [136,156], yet it exacerbates the challenge of confidence adjustment, particularly when working iteratively across prompting and output evaluation (as per §3.1).”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [136]
The citation is attached to the claim that a GenAI trade-off enables diversity of output. The Introduction and §5.1 discuss assessing algorithmic accuracy, not GenAI output diversity; searches for varied outputs and iterative prompting found no passage addressing that point. The later confidence-adjustment clause refers to the citing paper's §3.1 rather than this source. (LLM, conf=0.77)
Experimental — accuracy not yet measuredGenAI models are non-deterministic.
“Moreover, as noted, GenAI models are non-deterministic [136].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [136]
The Introduction discusses imperfect machine-learning algorithms, and the Procedure describes a machine-vision algorithm used to estimate jelly-bean counts. Neither passage addresses generative AI or whether models produce different outputs for the same input; searches for non-determinism, randomness, and repeated-input behavior found no supporting passage in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredCognitive forcing functions have been found to decrease humans' reliance on AI.
“These cognitive forcing functions are found to be able to decrease humans' reliance on AI.”
From Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making (2023) · cites this paper as [78]
Research on cognitive forcing has explored methods that push human decision-makers to spend more time deliberating about problems.
“research on”
From Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making (2023) · cites this paper as [78]
Cognitive forcing interventions have been found to reduce humans' reliance on AI.
“These cognitive forcing functions are found to be able to decrease humans' AI reliance.”
From Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making (2023) · cites this paper as [78]
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Shank, D. B. (2025) · Springer eBooks · doi:10.1007/978-3-031-86061-4_6
Guided Reflection in AI-Assisted Decision-Making: Effects on AI Overreliance and Decision Accuracy
Li, S., Li, J., Li, H., Zhu, H., & Li, X. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790632
Algorithms and decision making under riskPreprint
Liefooghe, B., Frenkel, N., & Mulder, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6875448
Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation
Lim, G., Tan, B. C. Z., Sim, K., Shi, W., Chew, M. H., Hee, M. S., Lee, R. K.-W., Perrault, S. T., & Choo, K. T. W. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3817008
Li, Z.-Y., Ma, Z.-Z., He, S.-S., Lu, Z.-R., Xiao, Z., & Yin, M. (2026) · Proceedings of the 2026 ACM Conference on Human-AI Complementarity and Alignment · doi:10.1145/3834580.3838738
User-Friendly AI-Based Decision Support Systems
Echeverry, N. (2026) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw71585.2026.11712078
- Authors
- Park, J. S., Barber, R., Kirlik, A., & Karahalios, K.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2019-11-07
- Volume
- 3
- Issue
- CSCW
- Pages
- 1-15
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W2983137008
- Cited by
- 65
- References
- 23
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 15 · 2023: 15 · 2022: 6 · 2021: 4
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- Funders
- National Science Foundation (NSF 1564041)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- slow technology, algorithmic decision support, algorithm speed, response time, algorithmic advice, reflection, human-algorithm interaction
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