Investigating How Experienced UX Designers Effectively Work with Machine Learning

Yang, Qian; Scuito, Alex; Zimmerman, John; Forlizzi, Jodi; Steinfeld, Aaron

doi:10.1145/3196709.3196730

Cited by 161 publications

(98 citation statements)

References 14 publications

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“…Although HCI researchers have developed methods for designing systems with human values in mind (e.g., [38,39,91,93]), they have not yet become best practices or even propagated into professional training for UX researchers. Moreover, there are significant challenges to adapting user-centered methods for AI systems (e.g., [5,26,89,90]).…”

Section: Engaging Diverse Stakeholdersmentioning

confidence: 99%

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Madaio

Stark

Vaughan

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

279

193

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Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We discuss aspects of organizational culture that may impact the efficacy of such checklists, and highlight future research directions.

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Section: Engaging Diverse Stakeholdersmentioning

confidence: 99%

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Madaio

Stark

Vaughan

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

279

193

View full text Add to dashboard Cite

show abstract

“…These insights on the narrativization of data and results add new dimensions to existing CSCW and HCI research on explainable machine learning systems [1,82,86,101] and human perception of data representations and algorithmic working [21,48,59,103], making visible not only the plurality of reasonings and modes of justification [8] that actually subtend applied data science work, but also the multiple forms of expertise that constitute such work in complex real-world settings. Workable data, for instance, is computationally accomplished through multiple forms of preprocessing-each attempt at reorganization adds value, but also removes possibilities.…”

Section: Collaboration Translation and Accountability: Implicationsmentioning

confidence: 99%

Trust in Data Science

Passi

Jackson

2018

Proc. ACM Hum.-Comput. Interact.

129

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The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.

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“…Machine learning was in this sense examined for its implications in design [58] and identified as an opportunity for user experience [30,104,105]. Yet, a large body of work in the machine learning research community has so far focused on constructing autonomous algorithms learning creative behaviour from large amounts of impersonal data-falling under the name of computational creativity [101].…”

Section: Creativity Support Toolsmentioning

confidence: 99%

Designing Deep Reinforcement Learning for Human Parameter Exploration

Scurto

Kerrebroeck

Caramiaux

et al. 2021

ACM Trans. Comput.-Hum. Interact.

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Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this article, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users for sound design. We describe a series of user-centred studies to probe the creative benefits of these agents and adapting their design to exploration. Preliminary studies observing users’ exploration strategies with parametric interfaces and testing different agent exploration behaviours led to the design of a fully-functioning prototype, called Co-Explorer, that we evaluated in a workshop with professional sound designers. We found that the Co-Explorer enables a novel creative workflow centred on human–machine partnership, which has been positively received by practitioners. We also highlight varied user exploration behaviours throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications.

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Investigating How Experienced UX Designers Effectively Work with Machine Learning

Cited by 161 publications

References 14 publications

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Trust in Data Science

Designing Deep Reinforcement Learning for Human Parameter Exploration

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