The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
“Discovering the sweet spot of human–computer configurations: a case study in information extraction,”
Abstract
Interactive intelligent systems, i.e., interactive systems that employ AI technologies, are currently present in many parts of our social, public and political life. An issue reoccurring often in the development of these systems is the question regarding the level of appropriate human and computer contributions. Engineers and designers lack a way of systematically defining and delimiting possible options for designing such systems in terms of levels of automation. In this paper, we propose, apply and reflect on a method for human-computer configuration design. It supports the systematic investigation of the design space for developing an interactive intelligent system. We illustrate our method with a use case in the context of collaborative ideation. Here, we developed a tool for information extraction from idea content. A challenge was to find the right level of algorithmic support, whereby the quality of the information extraction should be as high as possible, but, at the same time, the human effort should be low. Such contradicting goals are often an issue in system development; thus, our method proposed helped us to conceptualize and explore the design space. Based on a critical reflection on our method application, we want to offer a complementary perspective to the value-centered design of interactive intelligent systems. Our overarching goal is to contribute to the design of so-called hybrid systems where humans and computers are partners.
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“In the AI-first assistance pattern, where the user may not have an opportunity to form an independent assessment of the decision making problem, we observe cases (n = 10) in which users are given the ability to control when they want to receive the AI assistance via a request (Khadpe et al., 2020;Mackeprang et al., 2019;Baudel et al., 2021;Molina and Sundar, 2022;Gomez et al., 2023;Buçinca et al.,”
From Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review (2025) · cites this paper as Mackeprang et al., 2019
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A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 8×
“Based on the detailed description of the LoA proposed by Sheridan and Verplank [52] and the taxonomy proposed by Parasuraman and colleagues [48], we detailed each of the configurations by allocating the task to one or the other partner.”
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Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681
Power to the People: The Role of Humans in Interactive Machine Learning
Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014) · AI Magazine · doi:10.1609/aimag.v35i4.2513 · Cited by 1,084
Mixing Humans and Nonhumans Together: The Sociology of a Door-Closer
Johnson, J. (1988) · Social Problems · doi:10.2307/800624 · Cited by 892
The threat of algocracy: reality, resistance and accommodation
Danaher, J. (2016) · Philosophy & Technology · doi:10.1007/s13347-015-0211-1 · Cited by 519
Humans: Still Vital After All These Years of Automation
Parasuraman, R., & Wickens, C. D. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x312198 · Cited by 401
A Survey of Value Sensitive Design Methods
Friedman, B., Hendry, D. G., & Borning, A. (2017) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000015 · Cited by 389
Chatbots, Humbots, and the Quest for Artificial General Intelligence
Grudin, J., & Jacques, R. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300439 · Cited by 275
Value-Sensitive Algorithm Design: Method, Case Study, and Lessons
Zhu, H., Yu, B., Halfaker, A., & Terveen, L. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274463 · Cited by 220
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Cited by
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Capel, T., & Brereton, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580959 · Cited by 228
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Metadata
- Authors
- Mackeprang, M., Müller-Birn, C., & Stauss, M. T.
- Affiliations
- Freie Universität Berlin, Berlin, Germany
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2019-11-07
- Volume
- 3
- Issue
- CSCW
- Pages
- 1-30
- ISSN
- 2573-0142
- Cited by
- 39
- References
- 9
- Language
- en
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