research-article

Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction

Authors:

Maximilian Mackeprang,

Claudia Müller-Birn,

Maximilian Timo StaussAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 3, Issue CSCW

Article No.: 195, Pages 1 - 30

https://doi.org/10.1145/3359297

Published: 07 November 2019 Publication History

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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Index Terms

Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
    2. Interactive systems and tools

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 3, Issue CSCW

November 2019

5026 pages

EISSN:2573-0142

DOI:10.1145/3371885

Editors:
Airi Lampinen
Stockholm University, Sweden
,
Darren Gergle
Northwestern University, USA
,
David A. Shamma
FXPAL, USA

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Copyright © 2019 ACM.

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Published: 07 November 2019

Published in PACMHCI Volume 3, Issue CSCW

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Jiang HShi SZhang SZheng JLi Q(2025)SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-Based Synthetic Lethal PredictionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632531:1(919-929)Online publication date: Jan-2025
https://doi.org/10.1109/TVCG.2024.3456325
Park JWisniewski PSingh V(2024)Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data AnnotationSSRN Electronic Journal10.2139/ssrn.4774771Online publication date: 2024
https://doi.org/10.2139/ssrn.4774771
Joshi STolloczko CWenaas SHolm OLangved M(2024)Investigating How Generative AI Affects Decision-Making in Participatory Design: Shifting the space to make design choicesProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685384(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685384
Butcher BZilka MHron JCook DWeller A(2024)Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text DocumentsACM Journal on Responsible Computing10.1145/36525911:2(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3652591
Wan QHu SZhang YWang PWen BLu Z(2024)"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36373618:CSCW1(1-26)Online publication date: 26-Apr-2024
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