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Democratizing AI: Expert-Tested VPL-Based Prototype to Foster Participation

Published: 03 June 2024 Publication History

Abstract

This work explores the potential of Visual Programming Languages (VPLs) and no-code platforms to foster participation among users with limited computing experience in the design of ML-based systems. Conducting expert-based testing of the PyFlowML prototype, our preliminary research focuses on developing trustworthy ML-based prototypes that provide Explainable AI (XAI) techniques. Our evaluation employs heuristic methods and direct interactions with experts using the prototype. Utilizing cognitive walkthroughs with a think-aloud protocol, along with quantitative assessments such as task completion time and the System Usability Scale (SUS), our findings highlight the need of streamlining ML processes to enhance broader participation. These insights lay the groundwork for future research aimed at making the design of ML-based systems more accessible and collaborative through VPL-based tools.

References

[1]
E. Brynjolfsson and A. McAfee. 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. Book published by WW Norton & Company.
[2]
S. Dasgupta and B. M. Hill. 2017. Scratch Community Blocks: Supporting Children as Data Scientists. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Pages 3620-3631, Denver, Colorado, USA, CHI ’17., 12 pages. https://doi.org/10.1145/3025453.3025847
[3]
J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, 2013. Orange: Data Mining Toolbox in Python. In: Journal of Machine Learning Research, Volume 14, Number 1, Pages 2349-2353., 2349-2353 pages.
[4]
G. Fischer. 2021. End-User Development: Empowering Stakeholders with Artificial Intelligence, Meta-Design, and Cultures of Participation. In: Proceedings, Springer-Verlag, Berlin, Heidelberg. https://doi.org/10.1007/978-3-030-79840-6_1
[5]
S. Gallo, F. Paterno, and A. Malizia. 2023. Conversational Interfaces in IoT Ecosystems: Where We Are, What Is Still Missing. In: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, Pages 279-293, Vienna, Austria, MUM ’23., 15 pages. https://doi.org/10.1145/3626705.3627775
[6]
A. Halfaker and R. S. Geiger. 2020. ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia. In: Proc. ACM Hum.-Comput. Interact., Volume 4, CSCW2, Article 148, Pages 1-37., 37 pages. https://doi.org/10.1145/3415219
[7]
Y. N. Harari. 2018. Why Technology Favors Tyranny. In: The Atlantic, Volume 322, Number 3, Pages 64-73., 64-73 pages.
[8]
D. D. Hils. 1992. Visual Languages and Computing Survey: Data Flow Visual Programming Languages. In: Journal of Visual Languages & Computing, Volume 3, Number 1, Pages 69-101, 1992., 69-101 pages. https://doi.org/10.1016/1045-926X(92)90034-J
[9]
J. Lazar, J.H. Feng, and H. Hochheiser. 2017. Research methods in human-computer interaction. Publisher: Morgan Kaufmann.
[10]
H. Lieberman, F. Paternò, M. Klann, and V. Wulf. 2006. End-user Development: An Emerging Paradigm. In: End User Development, Pages 1-8, Springer., 8 pages.
[11]
M. Makhortykh, A. Urman, and R. Ulloa. 2021. Detecting Race and Gender Bias in Visual Representation of AI on Web Search Engines. In: Communications in Computer and Information Science, Pages 36-50, Springer International Publishing, 2021., 36-50 pages. https://doi.org/10.1007/978-3-030-78818-6_5
[12]
S. Makridakis. 2017. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. In: Futures, Volume 90, Pages 46-60. https://doi.org/10.1016/j.futures.2017.03.006
[13]
M. Mansoury, H. Abdollahpouri, M. Pechenizkiy, B. Mobasher, and R. Burke. 2020. Feedback Loop and Bias Amplification in Recommender Systems. In: arXiv.
[14]
S. Milano, M. Taddeo, and L. Floridi. 2020. Recommender systems and their ethical challenges. AI & SOCIETY, Volume 35, Issue 4, Pages 957–967. https://doi.org/10.1007/s00146-020-00950-y
[15]
F. Paternò and V. Wulf. 2017. New Perspectives in End-user Development. Book published by Springer.
[16]
Jeff Sauro and James R. Lewis. 2010. Average Task Times in Usability Tests: What to Report?In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10).
[17]
B. Shneiderman. 2022. Human-Centered AI. Book published by Oxford University Press.
[18]
B. Shneiderman, C. Plaisant, M. Cohen, S. Jacobs, N. Elmqvist, and N. Diakopoulos. 2016. Grand Challenges for HCI Researchers. In: Interactions, Volume 23, Number 5, Pages 24–25. https://doi.org/10.1145/2977645

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  • (2024)Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image AnalysisDiagnostics10.3390/diagnostics1421245114:21(2451)Online publication date: 1-Nov-2024

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cover image ACM Other conferences
AVI '24: Proceedings of the 2024 International Conference on Advanced Visual Interfaces
June 2024
578 pages
ISBN:9798400717642
DOI:10.1145/3656650
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

New York, NY, United States

Publication History

Published: 03 June 2024

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Author Tags

  1. AI democratization
  2. machine learning
  3. participation
  4. visual programming language

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AVI 2024

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AVI '24 Paper Acceptance Rate 21 of 82 submissions, 26%;
Overall Acceptance Rate 128 of 490 submissions, 26%

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View all
  • (2024)Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image AnalysisDiagnostics10.3390/diagnostics1421245114:21(2451)Online publication date: 1-Nov-2024

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