How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface

By René F. Kizilcec. International Conference on Human Factors in Computing Systems, 2016

623 citations recorded by the source corpus.

Subfields: Computer Science Applications, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/cf55db30b976d4e6dc8eaf4f10af322dfbf31bb7

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Citations in the corpus

'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “A notable exception is Kizilcec, who studied the effect of levels of transparency on perceptions of procedural justice [35].”
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns (2019, background): “More algorithmic transparency can lead to algorithmic disillusionment, where algorithms appear less powerful and useful but more fallible and inaccurate than previously thought (see also Kizilcec, 2016).”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “More recent work indicates other effects; explanations of how a system is working may lead to increased trust [40] but further explanations may be harmful to user perceptions.”
"Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media (2017, background): “User misunderstanding of algorithmically-driven curation systems has been documented [5, 10, 11, 29], with the implication that more accurate understandings might lead to increased user agency and success in achieving goals, as well as increased user trust in these systems [10, 21].”
Teachers' trust in AI-powered educational technology and a professional development program to improve it (2022, background): “Although we cannot make all teachers AI experts and the question of how much technical knowledge is enough for trust is still open (Kizilcec, 2016), the PDP should allow teachers to effectively judge the validity of the automated results and give them a perspective of what kind of errors the system…”
The ethics of algorithms: key problems and solutions (2020, neutral): “…ethical problems (Ananny and Crawford 2018): transparency can provide users with some critical information about the features and limitations of an algorithm, but it can also overwhelm users with information and thus, render the algorithm more opaque (Kizilcec 2016; Ananny and Crawford 2018).”
Human Trust in Artificial Intelligence: Review of Empirical Research (2020, background): “Kizilcec (2016) investigated trust in an algorithmic peer-reviewing system and found that when participants ’ expectations of their outcome were violated, the explanation regarding how the al-gorithm worked facilitated trust.”
Algorithmic Transparency in the News Media (2017, background): “More-over, expectation violation, which is thought to drive additional information seeking, was found to be a moderator in the effect of procedural transparency on increased trust (Kizilcec 2016).”
What Do We Want From Explainable Artificial Intelligence (XAI)? - A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research (2021, background): “Supporting this claim, Kizilcec [38] found that available explanatory information only affected perceived fairness when people’s expectations concerning an outcome were violated.”
User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability (2020, background): “The work of Kizilcec (2016) shows that using explanation can improve users’ overall satisfaction with a recommendation system.”
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “XAI is purported to change the belief in the trustworthiness of a system [181], while it should also support the trustworthy integration of systems [182].”
The Illusion of Control: Placebo Effects of Control Settings (2018, background): “Indeed, Kizilcec has shown that when users’ expectations are violated, they trust systems less — unless provided some transparency [29].”
Communicating Algorithmic Process in Online Behavioral Advertising (2018, methodology): “For example, providing students with too much or too little information about a grading algorithm both diminished students’ trust in the grading system [29].”