abstract

Emerging Perspectives in Human-Centered Machine Learning

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Gagan BansalAuthors Info & Claims

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: W11, Pages 1 - 8

https://doi.org/10.1145/3290607.3299014

Published: 02 May 2019 Publication History

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Abstract

Current Machine Learning (ML) models can make predictions that are as good as or better than those made by people. The rapid adoption of this technology puts it at the forefront of systems that impact the lives of many, yet the consequences of this adoption are not fully understood. Therefore, work at the intersection of people's needs and ML systems is more relevant than ever. This area of work, dubbed Human-Centered Machine Learning (HCML), re-thinks ML research and systems in terms of human goals. HCML gathers an interdisciplinary group of HCI and ML practitioners, each bringing their unique, yet related perspectives. This one-day workshop is a successor of Gillies et al. 2016 CHI Workshop and focuses on recent advancements and emerging areas in HCML. We aim to discuss different perspectives on these areas and articulate a coordinated research agenda for the XXI century.

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Schor BKallina ESingh JBlackwell A(2024)Meaningful Transparency for Clinicians: Operationalising HCXAI Research with GynaecologistsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658971(1268-1281)Online publication date: 3-Jun-2024
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Schor BNorval CCharlesworth ESingh J(2024)Mind The Gap: Designers and Standards on Algorithmic System Transparency for UsersProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642531(1-16)Online publication date: 11-May-2024
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Versino STurchi TMalizia A(2024)Leveraging Visual Languages to Foster User Participation in Designing Trustworthy Machine Learning Systems: A Comparative Study2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC60511.2024.00013(24-32)Online publication date: 2-Sep-2024
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Index Terms

Emerging Perspectives in Human-Centered Machine Learning
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design

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Information

Published In

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

3673 pages

ISBN:9781450359719

DOI:10.1145/3290607

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 02 May 2019

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SIGCHI

CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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April 26 - May 1, 2025

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Schor BKallina ESingh JBlackwell A(2024)Meaningful Transparency for Clinicians: Operationalising HCXAI Research with GynaecologistsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658971(1268-1281)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658971
Schor BNorval CCharlesworth ESingh J(2024)Mind The Gap: Designers and Standards on Algorithmic System Transparency for UsersProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642531(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642531
Versino STurchi TMalizia A(2024)Leveraging Visual Languages to Foster User Participation in Designing Trustworthy Machine Learning Systems: A Comparative Study2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC60511.2024.00013(24-32)Online publication date: 2-Sep-2024
https://doi.org/10.1109/VL/HCC60511.2024.00013
Gómez-Carmona OCasado-Mansilla DLópez-de-Ipiña DGarcía-Zubia J(2024)Human-in-the-loop machine learning: Reconceptualizing the role of the user in interactive approachesInternet of Things10.1016/j.iot.2023.10104825(101048)Online publication date: Apr-2024
https://doi.org/10.1016/j.iot.2023.101048
Ambe ARoomkham SLovell DBrereton M(2023)Rethinking the Design of Human-Data Interaction through a Study of Older Adults’ WellbeingProceedings of the 35th Australian Computer-Human Interaction Conference10.1145/3638380.3638451(266-279)Online publication date: 2-Dec-2023
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Lindley SWilkins D(2023)Building Knowledge through Action: Considerations for Machine Learning in the WorkplaceACM Transactions on Computer-Human Interaction10.1145/358494730:5(1-51)Online publication date: 23-Sep-2023
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Capel TBrereton M(2023)What is Human-Centered about Human-Centered AI? A Map of the Research LandscapeProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580959(1-23)Online publication date: 19-Apr-2023
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Chancellor S(2023)Toward Practices for Human-Centered Machine LearningCommunications of the ACM10.1145/353098766:3(78-85)Online publication date: 22-Feb-2023
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Ystgaard KAtzori LPalma DHeegaard PBertheussen LJensen MDe Moor K(2023)Review of the theory, principles, and design requirements of human-centric Internet of Things (IoT)Journal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04539-314:3(2827-2859)Online publication date: 4-Feb-2023
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Williams RMoskal MDe Halleux P(2022)ML Blocks: A Block-Based, Graphical User Interface for Creating TinyML Models2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC53370.2022.9833149(1-5)Online publication date: 12-Sep-2022
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Abstract

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Recommendations

Human-Centred Machine Learning

Introduction to the Special Issue on Human-Centered Machine Learning

Grounding Interactive Machine Learning Tool Design in How Non-Experts Actually Build Models

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