research-article

Making AI Machines Work for Humans in FoW

ACM SIGMOD Record, Volume 49, Issue 2

Pages 30 - 35

https://doi.org/10.1145/3442322.3442327

Published: 10 December 2020 Publication History

Abstract

The Future of Work (FoW) is witnessing an evolution where AI systems (broadly machines or businesses) are used to the benefit of humans. Work here refers to all forms of paid and unpaid labor in both physical and virtual workplaces and that is enabled by AI systems. This covers crowdsourcing platforms such as Amazon Mechanical Turk, online labor marketplaces such as TaskRabbit and Qapa, but also regular jobs in physical workplaces. Bringing humans back to the frontier of FoW will increase their trust in AI systems and shift their perception to use them as a source of self-improvement, ensure better work performance, and positively shape social and economic outcomes of a society and a nation. To enable that, physical and virtual workplaces will need to capture human traits, behavior, evolving needs, and provide jobs to all. Attitudes, values, opinions regarding the processes and policies will need to be assessed and considered in the design of FoW ecosystems.

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Cited By

Tamura TIto HOyama SMorishima A(2024)Influence of AI’s Uncertainty in the Dawid-Skene Aggregation for Human-AI CrowdsourcingWisdom, Well-Being, Win-Win10.1007/978-3-031-57867-0_17(232-247)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-57867-0_17
Kanda TIto HMorishima A(2022)Efficient Evaluation of AI Workers for the Human+AI Crowd Task Assignment2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020844(3995-4001)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020844
Fenwick AMolnar G(2022)The importance of humanizing AI: using a behavioral lens to bridge the gaps between humans and machinesDiscover Artificial Intelligence10.1007/s44163-022-00030-82:1Online publication date: 25-Aug-2022
https://doi.org/10.1007/s44163-022-00030-8
Show More Cited By

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Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 49, Issue 2

June 2020

57 pages

ISSN:0163-5808

DOI:10.1145/3442322

Editors:
Rada Chirkova
North Carolina State University
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Wim Martens
University of Bayreuth
,
Divesh Srivastava
ATT research
,
Pinar Tözü
IBM Almaden Research Center
,
Marianne Winslett
University of Illinois
,
Jun Yang
Duke University
,
Azza Abouzied
NYU
,
Lyublena Antova
Datometry
,
Aaron J. Elmore
University of Chicago
,
Kyriakos Mouratidis
Singapore Management University
,
Dan Olteanu
University of Oxford
,
Immanuel Trummer
Cornell University
,
Yannis Velegrakis
Utrecht University

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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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Publication History

Published: 10 December 2020

Published in SIGMOD Volume 49, Issue 2

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Cited By

Tamura TIto HOyama SMorishima A(2024)Influence of AI’s Uncertainty in the Dawid-Skene Aggregation for Human-AI CrowdsourcingWisdom, Well-Being, Win-Win10.1007/978-3-031-57867-0_17(232-247)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-57867-0_17
Kanda TIto HMorishima A(2022)Efficient Evaluation of AI Workers for the Human+AI Crowd Task Assignment2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020844(3995-4001)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020844
Fenwick AMolnar G(2022)The importance of humanizing AI: using a behavioral lens to bridge the gaps between humans and machinesDiscover Artificial Intelligence10.1007/s44163-022-00030-82:1Online publication date: 25-Aug-2022
https://doi.org/10.1007/s44163-022-00030-8
Enholm IPapagiannidis EMikalef PKrogstie J(2022)Artificial Intelligence and Business Value: a Literature ReviewInformation Systems Frontiers10.1007/s10796-021-10186-w24:5(1709-1734)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10796-021-10186-w
Amer-Yahia SBasu Roy SLi GLi ZIdreos SSrivastava D(2021)Data Management to Social Science and Back in the Future of WorkProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457536(2876-2877)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457536
Amiri MDuguépéroux JAllard TAgrawal DEl Abbadi A(2021)Separ: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy GuaranteesProceedings of the Web Conference 202110.1145/3442381.3449858(1891-1903)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449858
Votto AValecha RNajafirad PRao H(2021)Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature ReviewInternational Journal of Information Management Data Insights10.1016/j.jjimei.2021.1000471:2(100047)Online publication date: Nov-2021
https://doi.org/10.1016/j.jjimei.2021.100047

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