research-article

Open access

Using human-in-the-loop and explainable AI to envisage new future work practices

Authors:

Konstantinos Tsiakas,

Dave Murray-RustAuthors Info & Claims

PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments

Pages 588 - 594

https://doi.org/10.1145/3529190.3534779

Published: 11 July 2022 Publication History

All formats PDF

Abstract

In this paper, we discuss the trends and challenges of the integration of Artificial Intelligence (AI) methods in the workplace. An important aspect towards creating positive AI futures in the workplace is the design of fair, reliable and trustworthy AI systems which aim to augment human performance and perception, instead of replacing them by acting in an automatic and non-transparent way. Research in Human-AI Interaction has proposed frameworks and guidelines to design transparent and trustworthy human-AI interactions. Considering such frameworks, we discuss the potential benefits of applying human-in-the-loop (HITL) and explainable AI (XAI) methods to define a new design space for the future of work. We illustrate how such methods can create new interactions and dynamics between human users and AI in future work practices.

References

[1]

Imran Ahmed, Gwanggil Jeon, and Francesco Piccialli. 2022. From Artificial Intelligence to eXplainable Artificial Intelligence in Industry 4.0: A survey on What, How, and Where. IEEE Transactions on Industrial Informatics(2022).

[2]

Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, 2019. Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems. 1–13.

Digital Library

[3]

Jesse Josua Benjamin, Arne Berger, Nick Merrill, and James Pierce. 2021. Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–14.

Digital Library

[4]

José Castillo, Edith Galy, Pierre Thérouanne, and Raoul Do Nascimento. 2019. Study of the mental workload and stress generated using digital technology at the workplace. In H-Workload 2019: 3rd International Symposium on Human Mental Workload: Models and Applications (Works in Progress). 105.

[5]

Chien-Chun Chen, Chiu-Chi Wei, Su-Hui Chen, Lun-Meng Sun, and Hsien-Hong Lin. 2022. AI Predicted Competency Model to Maximize Job Performance. Cybernetics and Systems 53, 3 (2022), 298–317.

[6]

Pravin Chopade, David Edwards, Saad M Khan, Alejandro Andrade, and Scott Pu. 2019. CPSX: Using AI-Machine Learning for Mapping Human-Human Interaction and Measurement of CPS Teamwork Skills. In 2019 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 1–6.

[7]

Carina Dantas, Karolina Mackiewicz, Valentina Tageo, Giulio Jacucci, Diana Guardado, Sofia Ortet, Iraklis Varlamis, Michail Maniadakis, Eva de Lera, João Quintas, 2021. Benefits and Hurdles of AI In The Workplace–What Comes Next?International Journal of Artificial Intelligence and Expert Systems (2021).

[8]

Christos Emmanouilidis and Sabine Waschull. 2021. Human in the Loop of AI Systems in Manufacturing. In Trusted Artificial Intelligence in Manufacturing: A review of the emerging wave of ethical and human-centric AI technologies for smart prodution. NOW PUBLISHERS INC, 158–172.

[9]

Elisa Giaccardi and Johan Redström. 2020. Technology and more-than-human design. Design Issues 36, 4 (2020), 33–44.

[10]

Donna Haraway. 2016. Staying with the Trouble: Making Kin in the Chthulucene. edition.

[11]

Sara Hekkala and Riitta Hekkala. 2021. Integration of Artificial Intelligence into Recruiting Young Undergraduates: the Perceptions of 20–23-Year-Old Students. (2021).

[12]

Jung-Sing Jwo, Ching-Sheng Lin, and Cheng-Hsiung Lee. 2021. Smart technology–driven aspects for human-in-the-loop smart manufacturing. The International Journal of Advanced Manufacturing Technology 114, 5(2021), 1741–1752.

[13]

Lenneke Kuijer and Elisa Giaccardi. 2018. Co-performance: Conceptualizing the role of artificial agency in the design of everyday life. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13.

Digital Library

[14]

James Manyika and Kevin Sneader. 2018. AI, automation, and the future of work: Ten things to solve for. (2018).

[15]

Stephanie M Merritt. 2011. Affective processes in human–automation interactions. Human Factors 53, 4 (2011), 356–370.

[16]

Beth Porter and Francesca Grippa. 2020. A Platform for AI-Enabled Real-Time Feedback to Promote Digital Collaboration. Sustainability 12, 24 (2020), 10243.

[17]

Lionel P Robert, Casey Pierce, Liz Marquis, Sangmi Kim, and Rasha Alahmad. 2020. Designing fair AI for managing employees in organizations: a review, critique, and design agenda. Human–Computer Interaction 35, 5-6 (2020), 545–575.

[18]

Ben Shneiderman. 2020. Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction 36, 6(2020), 495–504.

[19]

Georgios Sofianidis, Jože M Rožanec, Dunja Mladenić, and Dimosthenis Kyriazis. 2021. A Review of Explainable Artificial Intelligence in Manufacturing. arXiv preprint arXiv:2107.02295(2021).

[20]

Konrad Sowa, Aleksandra Przegalinska, and Leon Ciechanowski. 2021. Cobots in knowledge work: Human–AI collaboration in managerial professions. Journal of Business Research 125 (2021), 135–142.

[21]

Harini Suresh, Steven R Gomez, Kevin K Nam, and Arvind Satyanarayan. 2021. Beyond expertise and roles: A framework to characterize the stakeholders of interpretable machine learning and their needs. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–16.

Digital Library

[22]

Jaime Teevan, B Hecht, and S Jaffe. 2020. The new future of work. Technical Report. Microsoft internal report.

[23]

Niels van Berkel, Mikael B Skov, and Jesper Kjeldskov. 2021. Human-AI interaction: intermittent, continuous, and proactive. Interactions 28, 6 (2021), 67–71.

Digital Library

[24]

Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019. Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–15.

Digital Library

[25]

Qian Yang, Nikola Banovic, and John Zimmerman. 2018. Mapping machine learning advances from hci research to reveal starting places for design innovation. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–11.

Digital Library

[26]

Qian Yang, Aaron Steinfeld, Carolyn Rosé, and John Zimmerman. 2020. Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 chi conference on human factors in computing systems. 1–13.

Digital Library

[27]

Rui Zhang, Nathan J McNeese, Guo Freeman, and Geoff Musick. 2021. ” An Ideal Human” Expectations of AI Teammates in Human-AI Teaming. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3(2021), 1–25.

Cited By

Reis MGonçalves JCortez PCarvalho MFernandes J(2025)A context-aware decision support system for selecting explainable artificial intelligence methods in business organizationsComputers in Industry10.1016/j.compind.2024.104233165(104233)Online publication date: Feb-2025
https://doi.org/10.1016/j.compind.2024.104233
Kalogiannidis SPatitsa CChalaris M(2024)The Integration of Artificial Intelligence in Business Communication Channels: Opportunities and ChallengesWSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS10.37394/23207.2024.21.15721(1922-1944)Online publication date: 30-Sep-2024
https://doi.org/10.37394/23207.2024.21.157
van Beek EGiaccardi EBoess SBozzon A(2023)The everyday enactment of interfaces: a study of crises and conflicts in the more-than-human homeHuman–Computer Interaction10.1080/07370024.2023.228353640:1-4(221-248)Online publication date: 30-Nov-2023
https://doi.org/10.1080/07370024.2023.2283536
Show More Cited By

Index Terms

Using human-in-the-loop and explainable AI to envisage new future work practices
1. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

Recommendations

Questioning the AI: Informing Design Practices for Explainable AI User Experiences
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic. While many recognize the necessity to incorporate explainability features in AI systems, how to address real-world user needs for understanding AI ...
"What is Your Envisioned Future?": Toward Human-AI Enrichment in Data Work of Asthma Care
CSCW

Patient-generated health data (PGHD) is crucial for healthcare providers' decision making, as it complements clinical data by providing a more holistic view of patients' daily conditions. We interviewed 20 healthcare providers in asthma care to envision ...
The effects of domain knowledge on trust in explainable AI and task performance: A case of peer-to-peer lending
Highlights
- We explored a human-centered approach to explainable AI by integrating expert domain knowledge.
Abstract
Increasingly, artificial intelligence (AI) is being used to assist complex decision-making such as financial investing. However, there are concerns regarding the black-box nature of AI algorithms. The field of explainable AI (XAI) has ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments

June 2022

704 pages

ISBN:9781450396318

DOI:10.1145/3529190

Conference Chair:
Fillia Makedon

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2022

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

PETRA '22

PETRA '22: The15th International Conference on PErvasive Technologies Related to Assistive Environments

June 29 - July 1, 2022

Corfu, Greece

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
2,311
Total Downloads

Downloads (Last 12 months)934
Downloads (Last 6 weeks)68

Reflects downloads up to 03 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Reis MGonçalves JCortez PCarvalho MFernandes J(2025)A context-aware decision support system for selecting explainable artificial intelligence methods in business organizationsComputers in Industry10.1016/j.compind.2024.104233165(104233)Online publication date: Feb-2025
https://doi.org/10.1016/j.compind.2024.104233
Kalogiannidis SPatitsa CChalaris M(2024)The Integration of Artificial Intelligence in Business Communication Channels: Opportunities and ChallengesWSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS10.37394/23207.2024.21.15721(1922-1944)Online publication date: 30-Sep-2024
https://doi.org/10.37394/23207.2024.21.157
van Beek EGiaccardi EBoess SBozzon A(2023)The everyday enactment of interfaces: a study of crises and conflicts in the more-than-human homeHuman–Computer Interaction10.1080/07370024.2023.228353640:1-4(221-248)Online publication date: 30-Nov-2023
https://doi.org/10.1080/07370024.2023.2283536
Mannmeusel JRothfelder MKhoshrou S(2023)Speeding Things Up. Can Explainability Improve Human Learning?Explainable Artificial Intelligence10.1007/978-3-031-44064-9_4(66-84)Online publication date: 30-Oct-2023
https://doi.org/10.1007/978-3-031-44064-9_4
Elbeialy HGhaith AElBaradei MAlkhodary MEissa AFarag EFdawey OEmad NElshenawy AShabaan MGamal M(2023)Visual Steering for Deep Neural Networks Using Explainable Artificial IntelligenceProceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 202310.1007/978-3-031-43247-7_4(43-52)Online publication date: 18-Sep-2023
https://doi.org/10.1007/978-3-031-43247-7_4
Yang K(2023)Emerging Organizational Changes in the 21st CenturyQuality in the Era of Industry 4.010.1002/9781119932475.ch8(281-313)Online publication date: 8-Dec-2023
https://doi.org/10.1002/9781119932475.ch8
Jafri FRauniyar KThapa SSiddiqui MKhushi MNaseem U(undefined)CHUNAV: Analyzing Hindi Hate Speech and Targeted Groups in Indian Election DiscourseACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3665245
https://dl.acm.org/doi/10.1145/3665245

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten