Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3613905.3643979acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
extended-abstract

Implications of Regulations on the Use of AI and Generative AI for Human-Centered Responsible Artificial Intelligence

Published: 11 May 2024 Publication History

Abstract

With the upcoming AI regulations (e.g., EU AI Act) and rapid advancements in generative AI, new challenges emerge in the area of Human-Centered Responsible Artificial Intelligence (HCR-AI). As AI becomes more ubiquitous, questions around decision-making authority, human oversight, accountability, sustainability, and the ethical and legal responsibilities of AI and their creators become paramount. Addressing these questions requires a collaborative approach. By involving stakeholders from various disciplines in the 2nd edition of the HCR-AI Special Interest Group (SIG) at CHI 2024, we aim to discuss the implications of regulations in HCI research, develop new theories, evaluation frameworks, and methods to navigate the complex nature of AI ethics, steering AI development in a direction that is beneficial and sustainable for all of humanity.

References

[1]
ACM Technology Policy Council. 2022. ACM Statement on Principles for Responsible Algorithmic Systems. https://www.acm.org/articles/bulletins/2022/november/tpc-statement-responsible-algorithmic-systems
[2]
ACM Technology Policy Council. 2023. Principles for the Development, Deployment, and Use of Generative AI Technologies. https://www.acm.org/binaries/content/assets/public-policy/ustpc-approved-generative-ai-principles
[3]
EU AI Act. 2021. Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. EU AI Act. Retrieved December 2023 from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
[4]
Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, and Gina Neff. 2022. Human-centered data science: an introduction. MIT Press, Cambridge, MA, US.
[5]
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang. 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. arxiv:1909.03012 [cs.AI]
[6]
Agathe Balayn, Mireia Yurrita, Jie Yang, and Ujwal Gadiraju. 2023. “Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery, New York, NY, USA, 482–495. https://doi.org/10.1145/3600211.3604674
[7]
Lex Beattie, Dan Taber, and Henriette Cramer. 2022. Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems. In Proceedings of the 16th ACM Conference on Recommender Systems. Association for Computing Machinery, New York, NY, USA, 528–530. https://doi.org/10.1145/3523227.3547403
[8]
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. Association for Computing Machinery, New York, NY, USA, 610–623.
[9]
Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report MSR-TR-2020-32. Microsoft. https://www.microsoft.com/en-us/research/publication/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai/
[10]
Corinne Cath. 2018. Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, 2133 (2018), 20180080. https://doi.org/10.1098/rsta.2018.0080
[11]
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 Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, USA, 473–484. https://doi.org/10.1145/3531146.3533113
[12]
Upol Ehsan, Philipp Wintersberger, Q. Vera Liao, Martina Mara, Marc Streit, Sandra Wachter, Andreas Riener, and Mark O. Riedl. 2021. Operationalizing Human-Centered Perspectives in Explainable AI. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems(CHI EA ’21). ACM, New York, NY, USA, Article 94, 6 pages. https://doi.org/10.1145/3411763.3441342
[13]
Upol Ehsan, Philipp Wintersberger, Q. Vera Liao, Elizabeth Anne Watkins, Carina Manger, Hal Daumé III, Andreas Riener, and Mark O Riedl. 2022. Human-Centered Explainable AI (HCXAI): Beyond Opening the Black-Box of AI. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems(CHI EA ’22). ACM, New York, NY, USA, Article 109, 7 pages. https://doi.org/10.1145/3491101.3503727
[14]
Jessica Fjeld, Nele Achten, Hannah Hilligoss, Adam Nagy, and Madhulika Srikumar. 2020. Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication 1, 2020-1 (2020), 1–39. https://doi.org/10.2139/ssrn.3518482
[15]
Batya Friedman and Helen Nissenbaum. 1996. Bias in Computer Systems. ACM Trans. Inf. Syst. 14, 3 (Jul 1996), 330–347. https://doi.org/10.1145/230538.230561
[16]
G7. 2023. Hiroshima Process International Code of Conduct for Advanced AI Systems. G7 leaders. Retrieved Novemeber 2023 from https://digital-strategy.ec.europa.eu/en/library/hiroshima-process-international-code-conduct-advanced-ai-systems
[17]
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. Datasheets for Datasets. Commun. ACM 64, 12 (Nov 2021), 86–92. https://doi.org/10.1145/3458723
[18]
White House. 2023. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. White House. Retrieved Novemeber 2023 from https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
[19]
IAAA. 2023. AI Auditing Protects Us All. International Association of Algorithmic Auditors. Retrieved December 2023 from https://iaaa-algorithmicauditors.org/
[20]
Min Kyung Lee, Nina Grgić-Hlača, Michael Carl Tschantz, Reuben Binns, Adrian Weller, Michelle Carney, and Kori Inkpen. 2020. Human-Centered Approaches to Fair and Responsible AI. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems(CHI EA ’20). ACM, New York, NY, USA, 1–8. https://doi.org/10.1145/3334480.3375158
[21]
Scott Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. https://doi.org/10.48550/arXiv.1705.07874
[22]
Interpret ML. 2019. A toolkit to help understand models and enable responsible machine learning. Interpret ML. Retrieved December 2023 from https://interpret.ml/
[23]
Yuri Nakao, Simone Stumpf, Subeida Ahmed, Aisha Naseer, and Lorenzo Strappelli. 2022. Toward Involving End-users in Interactive Human-in-the-loop AI Fairness. ACM Transactions on Interactive Intelligent Systems 12, 3 (July 2022), 18:1–18:30. https://doi.org/10.1145/3514258
[24]
National Institute of Standards and Technology. 2023. AI Risk Management Framework. NIST. Retrieved May 2023 from https://www.nist.gov/itl/ai-risk-management-framework
[25]
Filippo A Raso, Hannah Hilligoss, Vivek Krishnamurthy, Christopher Bavitz, and Levin Kim. 2018. Artificial Intelligence & Human Rights: Opportunities & Risks. Berkman Klein Center Research Publication 6, 2018-6 (2018), 1–63. https://doi.org/10.2139/ssrn.3259344
[26]
Brianna Richardson, Jean Garcia-Gathright, Samuel F. Way, Jennifer Thom, and Henriette Cramer. 2021. Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems(CHI ’21). ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3411764.3445604
[27]
David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, 2022. Tackling climate change with machine learning. ACM Computing Surveys (CSUR) 55, 2 (2022), 1–96. https://doi.org/10.1145/3485128
[28]
Ben Shneiderman. 2022. Human-centered AI. Oxford University Press, Oxford, UK.
[29]
Jessie J Smith, Lex Beattie, and Henriette Cramer. 2023. Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective. In Proceedings of the ACM Web Conference 2023. Association for Computing Machinery, New York, NY, USA, 3648–3659. https://doi.org/10.1145/3543507.3583204
[30]
Andrew Stranieri and Zhaohao Sun. 2022. A Process-Oriented Framework for Regulating Artificial Intelligence Systems. In Handbook of Research on Foundations and Applications of Intelligent Business Analytics. IGI Global, Hershey, PA, US, 96–112.
[31]
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Anna Korhonen, David Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, Florence, Italy, 3645–3650. https://doi.org/10.18653/v1/P19-1355
[32]
Lucy A. Suchman. 1987. Plans and situated actions: The problem of human-machine communication. Cambridge University Press, Cambridge, UK.
[33]
Mohammad Tahaei, Marios Constantinides, Daniele Quercia, Sean Kennedy, Michael Muller, Simone Stumpf, Q Vera Liao, Ricardo Baeza-Yates, Lora Aroyo, Jess Holbrook, 2023. Human-Centered Responsible Artificial Intelligence: Current & Future Trends. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–4. https://doi.org/10.1145/3544549.3583178
[34]
Mohammad Tahaei, Marios Constantinides, Daniele Quercia, and Michael Muller. 2023. A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI. arxiv:2302.05284 [cs.HC]
[35]
C Ten Holter, Lars Kunze, Jo-Ann Pattinson, Pericle Salvini, and Marina Jirotka. 2022. Responsible innovation; responsible data. A case study in autonomous driving. Journal of Responsible Technology 11 (2022), 100038.
[36]
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso. 2021. Deep Learning’s Diminishing Returns: The Cost of Improvement is Becoming Unsustainable. IEEE Spectrum 58, 10 (2021), 50–55. https://doi.org/10.1109/MSPEC.2021.9563954
[37]
Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Daniela Langhans, Max Tegmark, and Francesco Fuso Nerini. 2020. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications 11, 1 (2020), 1–10. https://doi.org/10.1038/s41467-019-14108-y
[38]
Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–16. https://doi.org/10.1145/3544548.3581278
[39]
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. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3544548.3580900

Cited By

View all
  • (2025)Exploring trends and topics in hybrid intelligence using keyword co-occurrence networks and topic modellingFutures10.1016/j.futures.2025.103550167(103550)Online publication date: Mar-2025
  • (2024)AI, CI, and Society 5.0Open AI and Computational Intelligence for Society 5.010.4018/979-8-3693-4326-5.ch001(1-26)Online publication date: 6-Sep-2024
  • (2024)Exploring Generative Postcard Futures with Older AdultsAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685502(1-6)Online publication date: 13-Oct-2024

Index Terms

  1. Implications of Regulations on the Use of AI and Generative AI for Human-Centered Responsible Artificial Intelligence

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        CHI EA '24: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
        May 2024
        4761 pages
        ISBN:9798400703317
        DOI:10.1145/3613905
        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: 11 May 2024

        Check for updates

        Author Tags

        1. AI ethics
        2. human-centered AI
        3. large language models
        4. regulations
        5. responsible AI

        Qualifiers

        • Extended-abstract
        • Research
        • Refereed limited

        Conference

        CHI '24

        Acceptance Rates

        Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

        Upcoming Conference

        CHI 2025
        ACM CHI Conference on Human Factors in Computing Systems
        April 26 - May 1, 2025
        Yokohama , Japan

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)731
        • Downloads (Last 6 weeks)140
        Reflects downloads up to 07 Mar 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2025)Exploring trends and topics in hybrid intelligence using keyword co-occurrence networks and topic modellingFutures10.1016/j.futures.2025.103550167(103550)Online publication date: Mar-2025
        • (2024)AI, CI, and Society 5.0Open AI and Computational Intelligence for Society 5.010.4018/979-8-3693-4326-5.ch001(1-26)Online publication date: 6-Sep-2024
        • (2024)Exploring Generative Postcard Futures with Older AdultsAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685502(1-6)Online publication date: 13-Oct-2024

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        Full Text

        HTML Format

        View this article in HTML Format.

        HTML Format

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media