Assessing demand for intelligibility in context-aware applications

By Brian Y. Lim, Anind K. Dey. 2009

279 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems and Management, Human-Computer Interaction

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “Lim and Dey (2009, 2010); and Lim, Dey, and Avrahami (2009).”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Lim and Dey [100] found via a series of user studies on context-aware applications that “Why not . . . ?” questions were common questions that people asked.”
The challenge of crafting intelligible intelligence (2018, background): “In brief, we seek AI systems where A) it is clear what factors caused the system’s action [24], allowing the users to predict how changes to the situation would have led to alternative behaviors, and B) permits effective control of the AI by enabling interaction.”
Explaining Explanations in AI (2018, neutral): “Explanations can be conceived as involving one or more explainers and explainees engaging in information transfer through dialogue, visual representation, or other means (Hilton, 1990), often to correct information or knowledge assymetry (Lim and Dey, 2009).”
Efficient Search for Diverse Coherent Explanations (2019, background): “1 Counterfactual explanations are referred to as “why-not explanations” by Lim and Dey (2009), and “sensitivity” by Binns et al. (2018).”
Evaluating XAI: A comparison of rule-based and example-based explanations (2021, background): “Human explanations tend to be contrastive: they compare a certain phenomenon (fact) with a hypothetical one (foil) [33,34].”
Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models (2020, background): “Additionally, in human-computer interaction researchers have worked to determine what users want from explanations (Lim and Dey, 2009), for instance by showing uses only a subset of text highlighted as important (as a simplifying step) (Bastings et al., 2019).”
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences (2021, methodology): “This notion is based on prior HCI work using prototypical questions to represent “intelligibility types” [65], and social science literature showing that people’s explanatory goals can be expressed in different kinds of”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “s behavior for non-experts, research on machine learning explanations studied how users interpret intelligent agents [28,93] and algorithms [99] to ﬁnd out what users expect from machine explanations [70,28]. A useful way of studying user comprehension of intelligent systems is to directly ask the u”
Questioning the AI: Informing Design Practices for Explainable AI User Experiences (2020, methodology): “We added three more types based on [44, 55, 56]– questions regarding model Input (training data), Output, and Performance.”
Toolkit to support intelligibility in context-aware applications (2010, methodology): “(1) An architecture for generating a wide range of explanations drawn from Lim & Dey [25], including Why, Why Not, How To, What, What If, Inputs, Outputs and Certainty.”
Too much, too little, or just right? Ways explanations impact end users' mental models (2013, methodology): “To objectively manipulate completeness, our treatments used a varying number of the intelligibility types identified by Lim and Dey [12]: inputs (features the system is aware of), model (an overview of the agent’s decision making process), why (the reasons underlying a specific decision), and…”
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models (2022, background): “Prior studies have examined how users interact with imperfect AI [11, 23, 26–28, 35, 37, 45].”
Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015, methodology): “One method for evaluating completeness is via Lim and Dey’s intelligibility types [29], with more complete explanations including more of these intelligibility types.”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “of why-oriented explanation systems in debugging a naı̈ve Bayes learning model for text classification and in context-aware applications were respectively tested in [345, 60] and [346, 78].”
Design of an intelligible mobile context-aware application (2011, unknown): “Lim, B.Y., & Dey, A.K. (2009).”