skip to main content
10.1145/3584931.3611279acmconferencesArticle/Chapter ViewAbstractPublication PagescscwConference Proceedingsconference-collections
extended-abstract
Public Access

Supporting User Engagement in Testing, Auditing, and Contesting AI

Published: 14 October 2023 Publication History

Abstract

In recent years, interest in directly involving end users in testing, auditing, and contesting AI systems has grown. The involvement of end users, especially from diverse backgrounds, can be essential to overcome AI developers’ blind spots and to surface issues that would otherwise go undetected prior to causing real-world harm. Emerging bodies of work in CSCW have begun to explore ways to engage end-users in testing and auditing AI systems, and to empower users to contest problematic or erroneous AI outputs. As this is a nascent area of work, we still know little about how to support effective user engagement. In this one-day workshop, we will bring together researchers and practitioners from academia, industry, and non-profit organizations to share ongoing efforts related to this workshop’s theme. Central to our discussions will be the challenges encountered in developing tools and processes to support user involvement, strategies to incentivize involvement, the asymmetric power dynamic between AI developers and end users, and the role of regulation in enhancing the accountability of AI developers and ameliorating potential burdens towards end-users. Overall, we hope the workshop will help shape the future of user engagement in building more responsible AI.

References

[1]
Open AI. 2022. ChatGPT Feedback Contest: Official Rules. https://cdn.openai.com/chatgpt/ChatGPT_Feedback_Contest_Rules.pdf
[2]
Kars Alfrink, Ianus Keller, Neelke Doorn, and Gerd Kortuem. 2023. Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–16.
[3]
Joshua Attenberg, Panos Ipeirotis, and Foster Provost. 2015. Beat the machine: Challenging humans to find a predictive model’s “unknown unknowns”. Journal of Data and Information Quality (JDIQ) 6, 1 (2015), 1–17.
[4]
Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. PMLR, 77–91.
[5]
Ángel Alexander Cabrera, Abraham J Druck, Jason I Hong, and Adam Perer. 2021. Discovering and validating ai errors with crowdsourced failure reports. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–22.
[6]
Stevie Chancellor, Eric PS Baumer, and Munmun De Choudhury. 2019. Who is the" human" in human-centered machine learning: The case of predicting mental health from social media. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1–32.
[7]
Rumman Chowdhury and Jutta Williams. 2021. Introducing Twitter’s first algorithmic bias bounty challenge. URl: https://blog. twitter. com/engineering/en_us/topics/insights/2021/algorithmic-bias-bountychallenge (2021).
[8]
Henriette Cramer, Jean Garcia-Gathright, Aaron Springer, and Sravana Reddy. 2018. Assessing and addressing algorithmic bias in practice. Interactions 25, 6 (2018), 58–63.
[9]
Wesley Hanwen Deng, Boyuan Guo, Alicia Devrio, Hong Shen, Motahhare Eslami, and Kenneth Holstein. 2023. Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–18.
[10]
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, and Haiyi Zhu. 2022. Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. In 2022 ACM Conference on Fairness, Accountability, and Transparency. ACM, Seoul Republic of Korea, 473–484. https://doi.org/10.1145/3531146.3533113
[11]
Wesley Hanwen Deng, Nur Yildirim, Monica Chang, Motahhare Eslami, Kenneth Holstein, and Michael Madaio. 2023. Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 705–716.
[12]
Alicia DeVos, Aditi Dhabalia, Hong Shen, Kenneth Holstein, and Motahhare Eslami. 2022. Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior. CHI Conference on Human Factors in Computing Systems (2022).
[13]
White House. 2022. Blueprint for an AI Bill of Rights. https://www.whitehouse.gov/ostp/ai-bill-of-rights/
[14]
Nicolas Kaufmann, Thimo Schulze, and Daniel Veit. 2011. More than fun and money. worker motivation in crowdsourcing–a study on mechanical turk. (2011).
[15]
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, 2021. Dynabench: Rethinking benchmarking in NLP. arXiv preprint arXiv:2104.14337 (2021).
[16]
Tzu-Sheng Kuo, Hong Shen, Jisoo Geum, Nev Jones, Jason I Hong, Haiyi Zhu, and Kenneth Holstein. 2023. Understanding Frontline Workers’ and Unhoused Individuals’ Perspectives on AI Used in Homeless Services. arXiv preprint arXiv:2303.09743 (2023).
[17]
Michelle S. Lam, Mitchell L. Gordon, Danaë Metaxa, Jeffrey T. Hancock, James A. Landay, and Michael S. Bernstein. 2022. End-User Audits: A System Empowering Communities to Lead Large-Scale Investigations of Harmful Algorithmic Behavior. Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 512 (Nov 2022), 34 pages. https://doi.org/10.1145/3555625
[18]
Min Kyung Lee, Daniel Kusbit, Anson Kahng, Ji Tae Kim, Xinran Yuan, Allissa Chan, Daniel See, Ritesh Noothigattu, Siheon Lee, Alexandros Psomas, 2019. WeBuildAI: Participatory framework for algorithmic governance. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1–35.
[19]
Rena Li, Sara Kingsley, Chelsea Fan, Proteeti Sinha, Nora Wai, Jaimie Lee, Hong Shen, Motahhare Eslami, and Jason Hong. 2023. Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–19.
[20]
Duri Long and Brian Magerko. 2020. What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–16.
[21]
Danaë Metaxa, Joon Sung Park, Ronald E Robertson, Karrie Karahalios, Christo Wilson, Jeff Hancock, Christian Sandvig, 2021. Auditing algorithms: Understanding algorithmic systems from the outside in. Foundations and Trends® in Human–Computer Interaction 14, 4 (2021), 272–344.
[22]
NIST. 2023. Artificial Intelligence Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework
[23]
Besmira Nushi, Ece Kamar, and Eric Horvitz. 2018. Towards accountable ai: Hybrid human-machine analyses for characterizing system failure. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, Vol. 6. 126–135.
[24]
Rodrigo Ochigame and Katherine Ye. 2021. Search Atlas: Visualizing Divergent Search Results Across Geopolitical Borders. In Designing Interactive Systems Conference 2021. 1970–1983.
[25]
Giada Pistilli. 2022. HuggingFace announcedthe new feature to flag any Model, Dataset, or Space on the Hub. https://twitter.com/GiadaPistilli/status/1571865167092396033?s=20&t=LRhhEu63s6ftPmtZdfz8Cw
[26]
Inioluwa Deborah Raji and Joy Buolamwini. 2019. Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. 429–435.
[27]
Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 33–44.
[28]
Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, and Saleema Amershi. 2023. Supporting Human-AI Collaboration in Auditing LLMs with LLMs. arXiv preprint arXiv:2304.09991 (2023).
[29]
Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and Discrimination: Converting Critical Concerns into Productive Inquiry (2014).
[30]
Hong Shen, Alicia DeVos, Motahhare Eslami, and Kenneth Holstein. 2021. Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–29.
[31]
Hong Shen, Leijie Wang, Wesley H Deng, Ciell Brusse, Ronald Velgersdijk, and Haiyi Zhu. 2022. The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 440–451.
[32]
Rumman Chowdhury Sven Cattell and Austin Carson. 2023. AI Village at DEF CON announces largest-ever public Generative AI Red Team. https://aivillage.org/
[33]
Latanya Sweeney. 2013. Discrimination in online ad delivery. Queue 11, 3 (2013), 10–29.
[34]
Qiaosi Wang, Michael Adam Madaio, Shivani Kapania, Shaun Kane, Michael Terry, Lauren Wilcox, 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. (2023).
[35]
Tris Warkentin and Josh Woodward. 2022. AI Test Kitchen. https://blog.google/technology/ai/join-us-in-the-ai-test-kitchen/
[36]
Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People+ AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–13.
[37]
Haiyi Zhu, Bowen Yu, Aaron Halfaker, and Loren Terveen. 2018. Value-sensitive algorithm design: Method, case study, and lessons. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–23.

Cited By

View all
  • (2025)Understanding Human-Centred AI: a review of its defining elements and a research agendaBehaviour & Information Technology10.1080/0144929X.2024.2448719(1-40)Online publication date: 16-Feb-2025
  • (2024)"Something Fast and Cheap" or "A Core Element of Building Trust"? - AI Auditing Professionals' Perspectives on Trust in AIProceedings of the ACM on Human-Computer Interaction10.1145/36869638:CSCW2(1-22)Online publication date: 8-Nov-2024
  • (2024)Collaboratively Designing and Evaluating Responsible AI InterventionsCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing10.1145/3678884.3687136(658-662)Online publication date: 11-Nov-2024
  • Show More Cited By

Index Terms

  1. Supporting User Engagement in Testing, Auditing, and Contesting AI

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CSCW '23 Companion: Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing
    October 2023
    596 pages
    ISBN:9798400701290
    DOI:10.1145/3584931
    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.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 October 2023

    Check for updates

    Author Tags

    1. algorithm auditing
    2. human-centered AI
    3. responsible AI
    4. usability testing

    Qualifiers

    • Extended-abstract
    • Research
    • Refereed limited

    Funding Sources

    Conference

    CSCW '23
    Sponsor:

    Acceptance Rates

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

    Upcoming Conference

    CSCW '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)276
    • Downloads (Last 6 weeks)45
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)Understanding Human-Centred AI: a review of its defining elements and a research agendaBehaviour & Information Technology10.1080/0144929X.2024.2448719(1-40)Online publication date: 16-Feb-2025
    • (2024)"Something Fast and Cheap" or "A Core Element of Building Trust"? - AI Auditing Professionals' Perspectives on Trust in AIProceedings of the ACM on Human-Computer Interaction10.1145/36869638:CSCW2(1-22)Online publication date: 8-Nov-2024
    • (2024)Collaboratively Designing and Evaluating Responsible AI InterventionsCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing10.1145/3678884.3687136(658-662)Online publication date: 11-Nov-2024
    • (2024)Transparency in the Wild: Navigating Transparency in a Deployed AI System to Broaden Need-Finding ApproachesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658985(1494-1514)Online publication date: 3-Jun-2024

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Login options

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media