UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

By G. Dove, Kim Halskov, Jodi L. Forlizzi, John Zimmerman. International Conference on Human Factors in Computing Systems, 2017

434 citations recorded by the source corpus.

Subfields: Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/df721fd78e29c06409730b0cac90f394da6bc96c

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In Human-Computer Interaction

Citations in the corpus

Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems (2019, background): “(3) We show that an AI-powered system tuned to avoid False Positive errors can lead to lower average perception of accuracy and lower user acceptance than one tuned to avoid False Negatives (even when both versions perform at the same overall accuracy).”
Trustworthy Artificial Intelligence: A Review (2022, background): “Some work has been done in this field that deals with post-development expectation management, which captures how different factors like information about the system, reasoning and understanding of the system, and first-hand experience of the system can affect the system’s acceptance [56, 113].”
Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design (2020, methodology): “This synthesis identiﬁed Figure 1: Mapping the human-AI interaction design challenges in the literature [58, 13, 26, 53] onto a user-centered design process (Double Diamond [10]) two sources of AI’s design complexities, and a framework that unravels their effects on design processes.”
Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher-AI Complementarity (2019, background): “To a certain extent, higher upfront technical investment may be unavoidable when prototyping complex, data-driven algorithmic systems (Dove et al., 2017).”
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI (2020, background): “Moreover, there are signiﬁcant challenges to adapting user-centered methods for AI systems (e.g., [5,26,89,90]).”
Design Principles for Generative AI Applications (2024, background): “Within the past few years, the emergence of AI as a design material [41, 46, 62, 191] has necessitated guidelines that inform its use.”
Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends (2021, methodology): “With this said, user experience design for ML systems and human – AI collaboration remains relatively rudimentary (Dove et al., 2017; Yang, 2018; Yang et al., 2020).”
Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, methodology): “Research shows that there are a lack of effective methods for designing AI systems and HCI professionals have had difficulty performing the typical HCI activities of conceptualization, rapid prototyping, and testing (Dove et al., 2017; Holmquist, 2017; Yang, Huang, et al., 2020; van Allen, 2018).”
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “This is part of a wider need to consider how to re-engineer better user experiences for multiple stakeholders interacting with machine learning systems in general [17].”
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “ss issues via de-contextualized quantitative metrics for individual ML models may be insufficient (cf. [96]). Future research should explore new approaches to auditing and prototyping ML systems (cf. [30]), perhaps aided by simulation tools that can help developers anticipate sensitive contexts or r”