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Co-Created Personas: Engaging and Empowering Users with Diverse Needs Within the Design Process
Personas are powerful tools for designing technology and envisioning its usage. They are widely used to imagine archetypal users around whom to orient design work. We have been exploring co-created personas as a technique to use in co-design with users who have diverse needs. Our vision was that this would broaden the demographic and liberate co-designers of their personal relationship with a health condition. This paper reports three studies where we investigated using co-created personas with people who had Parkinson’s disease, dementia or aphasia. Observational data of co-design sessions were collected and analysed. Findings revealed that the co-created personas encouraged users with diverse needs to engage with co-designing. Importantly, they also aforded additional benefts including empowering users within a more accessible design process. Refecting on the outcomes from the diferent user groups, we conclude with a discussion of the potential for co-created personas to be applied more broadly
Analyzing the Use of Camera Glasses in the Wild
Camera glasses enable people to capture point-of-view videos using a common
accessory, hands-free. In this paper, we investigate how, when, and why people
used one such product: Spectacles. We conducted 39 semi-structured interviews
and surveys with 191 owners of Spectacles. We found that the form factor
elicits sustained usage behaviors, and opens opportunities for new use-cases
and types of content captured. We provide a usage typology, and highlight
societal and individual factors that influence the classification of behaviors.Comment: In Proceedings of the 37th Annual ACM Conference on Human Factors in
Computing Systems (CHI 2019). ACM, New York, NY, US
Characterizing HCI Research in China: Streams, Methodologies and Future Directions
This position paper takes the first step to attempt to present the initial
characterization of HCI research in China. We discuss the current streams and
methodologies of Chinese HCI research based on two well-known HCI theories:
Micro/Marco-HCI and the Three Paradigms of HCI. We evaluate the discussion with
a survey of Chinese publications at CHI 2019, which shows HCI research in China
has less attention to Macro-HCI topics and the third paradigms of HCI
(Phenomenologically situated Interaction). We then propose future HCI research
directions such as paying more attention to Macro-HCI topics and third paradigm
of HCI, combining research methodologies from multiple HCI paradigms, including
emergent users who have less access to technology, and addressing the cultural
dimensions in order to provide better technical solutions and support
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in
Computing Systems (CHI 2019
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To relieve the pain of manually selecting machine learning algorithms and
tuning hyperparameters, automated machine learning (AutoML) methods have been
developed to automatically search for good models. Due to the huge model search
space, it is impossible to try all models. Users tend to distrust automatic
results and increase the search budget as much as they can, thereby undermining
the efficiency of AutoML. To address these issues, we design and implement
ATMSeer, an interactive visualization tool that supports users in refining the
search space of AutoML and analyzing the results. To guide the design of
ATMSeer, we derive a workflow of using AutoML based on interviews with machine
learning experts. A multi-granularity visualization is proposed to enable users
to monitor the AutoML process, analyze the searched models, and refine the
search space in real time. We demonstrate the utility and usability of ATMSeer
through two case studies, expert interviews, and a user study with 13 end
users.Comment: Published in the ACM Conference on Human Factors in Computing Systems
(CHI), 2019, Glasgow, Scotland U
Challenges in Collaborative HRI for Remote Robot Teams
Collaboration between human supervisors and remote teams of robots is highly
challenging, particularly in high-stakes, distant, hazardous locations, such as
off-shore energy platforms. In order for these teams of robots to truly be
beneficial, they need to be trusted to operate autonomously, performing tasks
such as inspection and emergency response, thus reducing the number of
personnel placed in harm's way. As remote robots are generally trusted less
than robots in close-proximity, we present a solution to instil trust in the
operator through a `mediator robot' that can exhibit social skills, alongside
sophisticated visualisation techniques. In this position paper, we present
general challenges and then take a closer look at one challenge in particular,
discussing an initial study, which investigates the relationship between the
level of control the supervisor hands over to the mediator robot and how this
affects their trust. We show that the supervisor is more likely to have higher
trust overall if their initial experience involves handing over control of the
emergency situation to the robotic assistant. We discuss this result, here, as
well as other challenges and interaction techniques for human-robot
collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019
Workshop: The Challenges of Working on Social Robots that Collaborate with
People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing
Systems, May 2019, Glasgow, U
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