The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
An explanation facility for today's expert systems
Abstract
The authors discuss explanation facility types found in most expert systems and shells used today, referring to these as practice systems, since they represent current practice in expert system work. Practice systems include nonresearch expert systems (being developed every day in industry) as well as nonresearch expert system shells. They maintain that an extensive explanation facility meeting the obligations of a practice system's role can be implemented using only practice technology. They discuss the relationship of journalism to explanation, showing that explanation facility tasks (roles) on current practice expert systems and shells resemble a newswriter's tasks, as both involve presenting users or readers with accurate, objective, noninteractive accounts of events. In news reporting, events are pieces of news. In expert-system explanation, events are actions taken by expert systems. The authors present a journalistic explanation facility, Joe, which is part of AGNESS (a generalized network-based expert system shell), under development at the University of Minnesota.>
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“While knowledge-based systems can provide clear explanations of the reasoning behind their outputs (Wick & Slagle, 1989; Gregor & Benbasat, 1999), models trained by machine learning algorithms may not (Lipton, 2015).”
From Algorithmic Accountability and Public Reason (2018) · cites this paper as Wick & Slagle, 1989
Citations
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Self-citation
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Cited by
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs
Wang, W., & Benbasat, I. (2007) · Journal of Management Information Systems · doi:10.2753/mis0742-1222230410 · Cited by 540
Explanations From Intelligent Systems: Theoretical Foundations and Implications for Practice1
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Algorithmic Accountability and Public Reason
Binns, R. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0263-5 · Cited by 457
“…systems can 9 Although, significant challenges in specifying constraints remain; see e.g. (Bostrom, 2003). provide clear explanations of the reasoning behind their outputs (Wick & Slagle, 1989; Gregor & Benbasat, 1999), models trained by machine learning algorithms may not (Lipton, 2015).”
BackgroundA systematic review and taxonomy of explanations in decision support and recommender systems
Nunes, I., & Jannach, D. (2017) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-017-9195-0 · Cited by 447
A review of explanation methods for Bayesian networks
Lacave, C., & Díez, F. J. (2002) · The Knowledge Engineering Review · doi:10.1017/s026988890200019x · Cited by 242
Metadata
- Authors
- Wick, M. R., & Slagle, J. R.
- Affiliations
- University of Minnesota (US)
- Venue
- IEEE Expert
- Published
- 1989
- Volume
- 4
- Issue
- 1
- Pages
- 26-36
- ISSN
- 0885-9000 (ISSN-L)
- OpenAlex
- W2014798403
- Cited by
- 68
- References
- 31
- Citations by year
- 2023: 2 · 2018: 1 · 2017: 4 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 6.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Expert (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Computational Techniques and Applications
- Keywords
- explanation facilities, expert systems, expert system shells
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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