1 research outputs found
Two Case Studies of Experience Prototyping Machine Learning Systems in the Wild
Throughout the course of my Ph.D., I have been designing the user experience
(UX) of various machine learning (ML) systems. In this workshop, I share two
projects as case studies in which people engage with ML in much more
complicated and nuanced ways than the technical HCML work might assume. The
first case study describes how cardiology teams in three hospitals used a
clinical decision-support system that helps them decide whether and when to
implant an artificial heart to a heart failure patient. I demonstrate that
physicians cannot draw on their decision-making experience by seeing only
patient data on paper. They are also confused by some fundamental premises upon
which ML operates. For example, physicians asked: Are ML predictions made based
on clinicians' best efforts? Is it ethical to make decisions based on previous
patients' collective outcomes? In the second case study, my collaborators and I
designed an intelligent text editor, with the goal of improving authors'
writing experience with NLP (Natural Language Processing) technologies. We
prototyped a number of generative functionalities where the system provides
phrase-or-sentence-level writing suggestions upon user request. When writing
with the prototype, however, authors shared that they need to "see where the
sentence is going two paragraphs later" in order to decide whether the
suggestion aligns with their writing; Some even considered adopting machine
suggestions as plagiarism, therefore "is simply wrong".
By sharing these unexpected and intriguing responses from these real-world ML
users, I hope to start a discussion about such previously-unknown complexities
and nuances of -- as the workshop proposal states -- "putting ML at the service
of people in a way that is accessible, useful, and trustworthy to all".Comment: This is an accepted position paper for the ACM CHI'19 Workshop
<Emerging Perspectives in Human-Centered Machine Learning