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SHAI 2023: Workshop on Designing for Safety in Human-AI Interactions

Published:27 March 2023Publication History

ABSTRACT

Generative ML models present a novel opportunity for a wider group of societal members to engage with AI, imagine new use cases, and applications with an increasing ability to disseminate the outcomes of such endeavors to larger audiences. However, owing to the novelty and despite best intentions, inadvertent outcomes might accrue leading to harms, especially to marginalized groups in society. As this field of Human AI Interaction advances, academic/industry researchers, and industry practitioners have an opportunity to brainstorm how to best utilize this new technology. Our workshop is aimed at such practitioners and researchers at the intersection of AI and HCI who are interested in collaboratively identifying challenges, and solutions to create safer outcomes with Generative ML models.

References

  1. Saleema Amershi, Max Chickering, Steven M Drucker, Bongshin Lee, Patrice Simard, and Jina Suh. 2015. Modeltracker: Redesigning performance analysis tools for machine learning. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 337–346.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Zinat Ara and Sungsoo Ray Hong. 2023. Exploring Design Space of Collaborative Career-Seeking Experience for People on Autism Spectrum. (2023).Google ScholarGoogle Scholar
  3. Amanda Baughan, Ashwin Rajadesingan, Alexis Hiniker, Paul Resnick, and Amy Bruckman. 2022. SIG on Designing for Constructive Conflict. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems(CHI EA ’22). Association for Computing Machinery, New York, NY, USA, 1–2.Google ScholarGoogle Scholar
  4. Minsuk Choi, Cheonbok Park, Soyoung Yang, Yonggyu Kim, Jaegul Choo, and Sungsoo Ray Hong. 2019. Aila: Attentive interactive labeling assistant for document classification through attention-based deep neural networks. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Alexandra Chouldechova and Aaron Roth. 2018. The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810 (2018).Google ScholarGoogle Scholar
  6. Jerry Alan Fails and Dan R Olsen Jr. 2003. Interactive machine learning. In Proceedings of the 8th international conference on Intelligent user interfaces. 39–45.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Laura Fearnley. 2023. Norms and Causation in Artificial Morality. (2023).Google ScholarGoogle Scholar
  8. Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. 2021. Gnes: Learning to explain graph neural networks. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 131–140.Google ScholarGoogle ScholarCross RefCross Ref
  9. Yuyang Gao, Tong Steven Sun, Liang Zhao, and Sungsoo Ray Hong. 2022. Aligning eyes between humans and deep neural network through interactive attention alignment. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1–28.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Nitesh Goyal, Ian Kivlichan, Rachel Rosen, and Lucy Vasserman. 2022. Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation. In Proceedings of ACM in Human Computer Interaction CSCW.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, and Meredith Ringel Morris. 2020. Toward fairness in AI for people with disabilities: A research roadmap. ACM SIGACCESS Accessibility and Computing125 (2020), 1–1.Google ScholarGoogle Scholar
  12. Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018. Women also snowboard: Overcoming bias in captioning models. In Proceedings of the European Conference on Computer Vision (ECCV). 771–787.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudik, and Hanna Wallach. 2019. Improving fairness in machine learning systems: What do industry practitioners need?. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Sungsoo Ray Hong, Jessica Hullman, and Enrico Bertini. 2020. Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs. In Proceedings of the ACM on Human-Computer Interaction CSCW, Vol. 4. 1–26.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. 2020. The hateful memes challenge: Detecting hate speech in multimodal memes. Advances in Neural Information Processing Systems 33 (2020), 2611–2624.Google ScholarGoogle Scholar
  16. Yubo Kou and Xinning Gui. 2021. Flag and Flaggability in Automated Moderation: The Case of Reporting Toxic Behavior in an Online Game Community. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan). 1–12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Toby Jia-Jun Li, Jingya Chen, Haijun Xia, Tom M Mitchell, and Brad A Myers. 2020. Multi-modal repairs of conversational breakdowns in task-oriented dialogs. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology. 1094–1107.Google ScholarGoogle Scholar
  18. Neil McDonnell. 2023. The Philosophy of X in XAI. In Proceedings of the ACM IUI Workshops, Vol. 2023.Google ScholarGoogle Scholar
  19. Meta. 2022. BlenderBot 3: An AI Chatbot That Improves Through Conversation. Retrieved 02 23 2023 from https://about.fb.com/news/2022/08/blenderbot-ai-chatbot-improves-through-conversation/Google ScholarGoogle Scholar
  20. Andrew Sellars. 2016. Defining hate speech. Berkman Klein Center Research Publication2016-20 (2016), 16–48.Google ScholarGoogle ScholarCross RefCross Ref
  21. Sarah Tan, Julius Adebayo, Kori Inkpen, and Ece Kamar. 2018. Investigating human+ machine complementarity for recidivism predictions. CoRR abs/1808.09123 (2018).Google ScholarGoogle Scholar
  22. Almira Osmanovic Thunström. 2022. We Asked GPT-3 to Write an Academic Paper about Itself—Then We Tried to Get It Published. Retrieved 02 23 2023 from https://www.scientificamerican.com/article/we-asked-gpt-3-to-write-an-academic-paper-about-itself-mdash-then-we-tried-to-get-it-published/Google ScholarGoogle Scholar
  23. James Vincent. 2022. YouTuber trains AI bot on 4chan’s pile o’ bile with entirely predictable results. https://www.theverge.com/2022/6/8/23159465/youtuber-ai-bot-pol-gpt-4chan-yannic-kilcher-ethicsGoogle ScholarGoogle Scholar
  24. Dakuo Wang, Liuping Wang, Zhan Zhang, Ding Wang, Haiyi Zhu, Yvonne Gao, Xiangmin Fan, and Feng Tian. 2021. “Brilliant AI Doctor” in Rural Clinics: Challenges in AI-Powered Clinical Decision Support System Deployment. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Tris Warkentin and Josh Woodward. 2022. Join us in the AI Test Kitchen. Retrieved 02 23 2023 from https://blog.google/technology/ai/join-us-in-the-ai-test-kitchen/Google ScholarGoogle Scholar
  26. Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong, Sijia Liu, and Sanmi Koyejo. 2022. A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction. NAACL (2022).Google ScholarGoogle Scholar
  27. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457 (2017).Google ScholarGoogle Scholar

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        • Published in

          cover image ACM Conferences
          IUI '23 Companion: Companion Proceedings of the 28th International Conference on Intelligent User Interfaces
          March 2023
          266 pages
          ISBN:9798400701078
          DOI:10.1145/3581754

          Copyright © 2023 Owner/Author

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          • Published: 27 March 2023

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