Steps to take before intelligent user interfaces become real

By Kristina Höök. Interacting with computers, 2000

363 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/8efbe7027ad7ddbf88213f8fadd508ab41385115

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

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Citations in the corpus

Power to the People: The Role of Humans in Interactive Machine Learning (2014, background): “, Norman, 1994; Höök, 2000; Horvitz, 1999; Jameson, 2009). For example, Norman (1994) and Höök (2000) both identified safety and trust as key factors to consider when designing intelligent systems, referring to the assurance against and prevention of unwanted adaptations or actions. Others have stat”
Adaptive interfaces and agents (2002, background): “Discussions of these problems have been presented by a number of authors (see, e.g., Höök, 2000; Lanier, 1995; Norman, 1994; Schaumburg, 2001; Shneiderman, 1995; Wexelblat & Maes, 1997).”
Recommender Systems in Technology Enhanced Learning (2011, background): “• First, adequately defining the reference variables against which the adaptivity of the system will be evaluated is difficult for those systems that either cannot switch off the adaptivity, or where a non-adaptive version appears to be absurd because adaptivity is an inherent feature of these syste”
Toward establishing trust in adaptive agents (2008, background): “The guidelines that we present here build on the broad themes of transparency and trust mentioned in [14] and provide more grounded, concrete guidance for designing interfaces for adaptive agents.”
The effects of transparency on trust in and acceptance of a content-based art recommender (2008, background): “Other prior studies also suggest that making adaptive systems more transparent to the user could lead to a more positive user attitude towards using a system and increases in system performance (e.g. Kay 2006; Höök et al. 1996; Höök 2000; Carmagnola et al. 2006; Gregor and Benbasat 1999).”
Designing Contestability: Interaction Design, Machine Learning, and Mental Health (2017, background): “In recent years, these systems have increasingly “become real” [10], finding widespread adoption and media attention.”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “parency [208] and interactive machine learning [6]. Others suggest a set of theoretical and design principles to support intelligibility of intelligent system and accountability of human users (e.g., [16, 90]). In a recent survey, Abdul et al. [1] presented a thorough literature analysis to find XAI”
Guidelines for Human-AI Interaction (2019, background): “Others recommended managing expectations so as not to mislead or frustrate users during interaction with unpredictable adaptive agents [16, 20, 33].”