Toolkit to support intelligibility in context-aware applications

By Brian Y. Lim, Anind K. Dey. 2010

194 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems and Management

How researchers see this paper

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

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Information Systems and Management

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).”
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.”
You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems (2014, background): “Work by Lim and Dey has resulted in a toolkit for applications to generate explanations for popular machine learning systems [38], and a few systems add debugging capabilities to explanations [2], [33], [34].”
Mini-crowdsourcing end-user assessment of intelligent assistants: A cost-benefit study (2011, background): “Recent work by Lim and Dey has resulted in a toolkit for applications to generate explanations for popular machine learning systems [15], and a few systems add debugging capabilities to explanations [10, 11].”
Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach (2011, background): “…system (1) advises the user about which predictions to test, then (2) contributes more tests “like” the user’s, (3) measures how much of the assistant’s reasoning has been tested, and (4) continually monitors over time whether previous testing still “covers” new behaviors the assistant has learned.”
Justification Narratives for Individual Classifications (2014, background): “, 2000; Tintarev and Masthoff, 2007) and Context-Aware Applications (Lim and Dey, 2010; Vermeulen, 2010).”
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent (2012, background): “Recent research has made inroads into supporting this type of functionality [1,11,14,16].”
Context Aware Computing for The Internet of Things: A Survey (2013, methodology): “We present the results of a survey conducted by Lim and Dey [108] in Figure 7.”
Explanation and Justification in Machine Learning : A Survey Or (2017, methodology): “[Lim and Dey, 2010] developed a toolkit for use in context-aware applications that provides eight types of explanation for four of the most common model types (rules, decision trees, naı̈ve Bayes and HMMs).”
The Role of Explanations in Assessing and Correcting Personalized Intelligent Agents (2012, background): “Recent research has made inroads into supporting this type of functionality [1, 2, 5, 7], but debugging can be difficult for even trained software developers—helping end users, who have knowledge of neither software engineering nor machine learning, is no trivial task.”
Verbalization: Narration of Autonomous Robot Experience (2016, methodology): “Due to the success of intelligibility across many applications, intelligibility toolkits have been implemented for consistency of explanation across different machine learning algorithms [Lim and Dey, 2010].”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “…have been proposed to aid the development of explainable ambient intelligence: PersonisAD for scrutable personalization [8], Per-sonisLF for long-term user models with forgetting [10], En-cators to explain context reasoning [41], and the Intelligibility Toolkit to explain question types [113].”
The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems (2015, background): “Providing a Confidence explanation for a suggestion in the form of a percentage is common practice in CDSS design and has been included in design guidelines for increasing system intelligibility [20].”
Designing Theory-Driven User-Centric Explainable AI (2019, background): “Lim and Dey identified several queries (called intelligibility queries) that a user may ask of a smart system [65, 66].”
Crossing the bridge over Norman's Gulf of Execution: revealing feedforward's true identity (2013, background): “Lim and Dey have developed a toolkit [18] to support “What if?” questions in context-aware systems.”