ABSTRACT
Artificial Intelligence (AI) can refer to the machine learning algorithms and the automation applications built on top of these algorithms. Human-computer interaction (HCI) researchers have studied these AI applications and suggested various Human-Centered AI (HCAI) principles for an explainable, safe, reliable, and trustworthy interaction experience. While some designers believe that computers should be supertools and active appliances, others believe that these latest AI systems can be collaborators. With today’s AI algorithm breakthroughs, in this panel we ask whether the supertool or the collaboration metaphors best support work and play? How can we design AI systems to work best with people or for people? What does it take to get there? This panel will bring together panelists with diverse backgrounds to engage the audience through the discussion of their shared or diverging visions on the future of human-AI interaction design.
- 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.Google ScholarDigital Library
- Carrie J Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019. ” Hello AI”: Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proceedings of the ACM on Human-computer Interaction 3, CSCW(2019), 1–24.Google ScholarDigital Library
- Elizabeth F Churchill. 2020. HCI and UX as translational research. Interactions 27, 5 (2020), 22–23.Google ScholarDigital Library
- Lucas Colusso, Ridley Jones, Sean A Munson, and Gary Hsieh. 2019. A translational science model for HCI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
- Valdemar Danry, Pat Pataranutaporn, Yaoli Mao, and Pattie Maes. 2020. Wearable Reasoner: Towards Enhanced Human Rationality Through A Wearable Device With An Explainable AI Assistant. In Proceedings of the Augmented Humans International Conference. 1–12.Google ScholarDigital Library
- Paul Dourish and Victoria Bellotti. 1992. Awareness and Coordination in Shared Workspaces. In Proceedings of the 1992 ACM Conference on Computer-supported Cooperative Work (Toronto, Ontario, Canada) (CSCW ’92). ACM, New York, NY, USA, 107–114. https://doi.org/10.1145/143457.143468Google ScholarDigital Library
- Jaimie et al. Drozdal. 2020. Exploring Information Needs for Establishing Trust in Automated Data Science Systems. In IUI’20. ACM, in press.Google Scholar
- Thomas Erickson and Wendy A Kellogg. 2000. Social translucence: an approach to designing systems that support social processes. ACM transactions on computer-human interaction (TOCHI) 7, 1(2000), 59–83.Google Scholar
- Umer Farooq, Jonathan Grudin, Ben Shneiderman, Pattie Maes, and Xiangshi Ren. 2017. Human computer integration versus powerful tools. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 1277–1282.Google ScholarDigital Library
- S Larry Goldenberg, Guy Nir, and Septimiu E Salcudean. 2019. A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology 16, 7 (2019), 391–403.Google ScholarCross Ref
- Jonathan Grudin. 1988. Why CSCW applications fail: problems in the design and evaluationof organizational interfaces. In Proceedings of the 1988 ACM conference on Computer-supported cooperative work. 85–93.Google ScholarDigital Library
- Jonathan Grudin. 2017. From tool to partner: The evolution of human-computer interaction. Synthesis Lectures on Human-Centered Interaction 10, 1(2017), i–183.Google Scholar
- Guy Hoffman and Cynthia Breazeal. 2004. Collaboration in human-robot teams. In AIAA 1st Intelligent Systems Technical Conference. 6434.Google ScholarCross Ref
- Eric Horvitz. 1999. Principles of mixed-initiative user interfaces. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems. 159–166.Google ScholarDigital Library
- Jeamin Koo, Jungsuk Kwac, Wendy Ju, Martin Steinert, Larry Leifer, and Clifford Nass. 2015. Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing (IJIDeM) 9, 4(2015), 269–275.Google ScholarCross Ref
- Yezdi Lashkari, Max Metral, and Pattie Maes. 1994. Collaborative interface agents. In AAAI, Vol. 94. 444–449.Google Scholar
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436–444.Google Scholar
- Joseph CR Licklider. 1960. Man-computer symbiosis. IRE transactions on human factors in electronics1 (1960), 4–11.Google Scholar
- Clifford Nass, Jonathan Steuer, and Ellen R Tauber. 1994. Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems. 72–78.Google ScholarDigital Library
- Judith S Olson and Gary M Olson. 2013. Working together apart: Collaboration over the internet. Synthesis Lectures on Human-Centered Informatics 6, 5(2013), 1–151.Google ScholarCross Ref
- Xiangshi Ren, Chaklam Silpasuwanchai, and John Cahill. 2019. Human-engaged computing: the future of human–computer interaction. CCF transactions on pervasive computing and interaction 1, 1(2019), 47–68.Google Scholar
- Ben Shneiderman. 2020. Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction 36, 6(2020), 495–504.Google Scholar
- Ben Shneiderman and Pattie Maes. 1997. Direct manipulation vs. interface agents. interactions 4, 6 (1997), 42–61.Google Scholar
- Eliza Strickland. 2019. IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI health care. IEEE Spectrum 56, 4 (2019), 24–31.Google ScholarCross Ref
- Loren G Terveen. 1995. Overview of human-computer collaboration. Knowledge-Based Systems 8, 2-3 (1995), 67–81.Google ScholarDigital Library
- Dakuo Wang, Josh Andres, Justin Weisz, Erick Oduor, and Casey Dugan. 2021. AutoDS: Towards Human-Centered Automation of Data Science. In Proceedings of the CHI 2021.Google ScholarDigital Library
- Dakuo Wang, Liuping Wang, Zhan Zhang, Ding Wang, Haiyi Zhu, Yvonne Gao, Xiangmin Fan, and Feng Tian. 2021. Brilliant AI Doctor in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment. In Proceedings of the CHI 2021.Google Scholar
- Dakuo Wang, Justin D Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019. Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proceedings of the ACM on Human-Computer Interaction 3, CSCW(2019), 1–24.Google ScholarDigital Library
- Ying Xu, Dakuo Wang, Penelope Collins, Hyelim Lee, and Mark Warschauer. [n.d.]. Same benefits, different communication patterns: Comparing Children’s reading with a conversational agent vs. a human partner. Computers & Education 161 ([n. d.]), 104059.Google Scholar
Index Terms
- Designing AI to Work WITH or FOR People?
Recommendations
From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People
CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing SystemsArtificial Intelligent (AI) and Machine Learning (ML) algorithms are coming out of research labs into the real-world applications, and recent research has focused a lot on Human-AI Interaction (HAI) and Explainable AI (XAI). However, Interaction is not ...
“Brilliant AI Doctor” in Rural Clinics: Challenges in AI-Powered Clinical Decision Support System Deployment
CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing SystemsArtificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making ...
Methods and standards for research on explainable artificial intelligence: Lessons from intelligent tutoring systems
AbstractThe DARPA Explainable Artificial Intelligence (AI) (XAI) Program focused on generating explanations for AI programs that use machine learning techniques. This article highlights progress during the DARPA Program (2017‐2021) relative to research ...
Lessons learned in the work on intelligent tutoring systems that apply to system design in Explainable AI. image image
Comments