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short-paper

Interrogating Data Science

Authors:

Michael Muller,

Cecilia Aragon,

Cathrine Seidelin,

Anissa TanweerAuthors Info & Claims

CSCW '20 Companion: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing

Pages 467 - 473

https://doi.org/10.1145/3406865.3418584

Published: 17 October 2020 Publication History

Abstract

Data science provides powerful tools and methods. CSCW researchers have contributed insightfulstudies of conventional work-practices in data science - and particularly machine learning. However,recent research has shown that human skills and collaborative decision-making, play important rolesin defining data, acquiring data, curating data, designing data, and creating data. This workshopgathers researchers and practitioners together to take a collective and critical look at data sciencework-practices, and at how those work-practices make crucial and often invisible impacts on theformal work of data science. When we understand the human and social contributions to data sciencepipelines, we can constructively redesign both work and technologies for new insights, theories, andchallenges.

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Cited By

Yang TMiceli M(2024)"Guilds" as Worker Empowerment and Control in a Chinese Data Work PlatformProceedings of the ACM on Human-Computer Interaction10.1145/36869048:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686904
Alvarado Garcia AWong-Villacres MHernández BLe Dantec C(2024)Bitacora: A Toolkit for Supporting NonProfits to Critically Reflect on Social Media Data UseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642673(1-29)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642673
Lin CJackson S(2023)From Bias to Repair: Error as a Site of Collaboration and Negotiation in Applied Data Science WorkProceedings of the ACM on Human-Computer Interaction10.1145/35796077:CSCW1(1-32)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579607
Show More Cited By

Index Terms

Interrogating Data Science
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing

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Published In

cover image ACM Conferences

CSCW '20 Companion: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing

October 2020

559 pages

ISBN:9781450380591

DOI:10.1145/3406865

General Chairs:
Matthew Bietz
University of California, Irvine
,
Andrea Wiggins

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 17 October 2020

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CSCW '20

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SIGCHI

CSCW '20: Computer Supported Cooperative Work and Social Computing

October 17 - 21, 2020

Virtual Event, USA

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Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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CSCW '25

Sponsor:
sigchi

Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

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Cited By

Yang TMiceli M(2024)"Guilds" as Worker Empowerment and Control in a Chinese Data Work PlatformProceedings of the ACM on Human-Computer Interaction10.1145/36869048:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686904
Alvarado Garcia AWong-Villacres MHernández BLe Dantec C(2024)Bitacora: A Toolkit for Supporting NonProfits to Critically Reflect on Social Media Data UseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642673(1-29)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642673
Lin CJackson S(2023)From Bias to Repair: Error as a Site of Collaboration and Negotiation in Applied Data Science WorkProceedings of the ACM on Human-Computer Interaction10.1145/35796077:CSCW1(1-32)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579607
Alvarado Garcia AWong-Villacres MMiceli MHernández BLe Dantec C(2023)Mobilizing Social Media Data: Reflections of a Researcher Mediating between Data and OrganizationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580916(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580916
Poole A(2023) Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice Proceedings of the Association for Information Science and Technology10.1002/pra2.79360:1(338-352)Online publication date: 22-Oct-2023
https://doi.org/10.1002/pra2.793
Rothschild AMeng ADiSalvo CJohnson BShapiro BDiSalvo B(2022)Interrogating Data Work as a Community of PracticeProceedings of the ACM on Human-Computer Interaction10.1145/35551986:CSCW2(1-28)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555198
Cambo SGergle D(2022)Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data ScienceProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501998(1-19)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501998
Tanweer AAragon CMuller MGuha SPassi SNeff GKogan M(2022)Interrogating Human-centered Data Science: Taking Stock of Opportunities and LimitationsExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3503740(1-6)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3503740
Boyd K(2021)Datasheets for Datasets help ML Engineers Notice and Understand Ethical Issues in Training DataProceedings of the ACM on Human-Computer Interaction10.1145/34795825:CSCW2(1-27)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3479582
Scheuerman MHanna ADenton E(2021)Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset DevelopmentProceedings of the ACM on Human-Computer Interaction10.1145/34760585:CSCW2(1-37)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3476058

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