41 research outputs found
From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Self
In the rapidly evolving landscape of AI-mediated communication (AIMC), tools
powered by Large Language Models (LLMs) are becoming integral to interpersonal
communication. Employing a mixed-methods approach, we conducted a one-week
diary and interview study to explore users' perceptions of these tools' ability
to: 1) support interpersonal communication in the short-term, and 2) lead to
potential long-term effects. Our findings indicate that participants view AIMC
support favorably, citing benefits such as increased communication confidence,
and finding precise language to express their thoughts, navigating linguistic
and cultural barriers. However, the study also uncovers current limitations of
AIMC tools, including verbosity, unnatural responses, and excessive emotional
intensity. These shortcomings are further exacerbated by user concerns about
inauthenticity and potential overreliance on the technology. Furthermore, we
identified four key communication spaces delineated by communication stakes
(high or low) and relationship dynamics (formal or informal) that
differentially predict users' attitudes toward AIMC tools. Specifically,
participants found the tool is more suitable for communicating in formal
relationships than informal ones and more beneficial in high-stakes than
low-stakes communication
A Framework for Designing Fair Ubiquitous Computing Systems
Over the past few decades, ubiquitous sensors and systems have been an
integral part of humans' everyday life. They augment human capabilities and
provide personalized experiences across diverse contexts such as healthcare,
education, and transportation. However, the widespread adoption of ubiquitous
computing has also brought forth concerns regarding fairness and equitable
treatment. As these systems can make automated decisions that impact
individuals, it is essential to ensure that they do not perpetuate biases or
discriminate against specific groups. While fairness in ubiquitous computing
has been an acknowledged concern since the 1990s, it remains understudied
within the field. To bridge this gap, we propose a framework that incorporates
fairness considerations into system design, including prioritizing stakeholder
perspectives, inclusive data collection, fairness-aware algorithms, appropriate
evaluation criteria, enhancing human engagement while addressing privacy
concerns, and interactive improvement and regular monitoring. Our framework
aims to guide the development of fair and unbiased ubiquitous computing
systems, ensuring equal treatment and positive societal impact.Comment: 8 pages, 1 figure, published in 2023 ACM International Joint
Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International
Symposium on Wearable Computin
Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model
Despite the plethora of telehealth applications to assist home-based older
adults and healthcare providers, basic messaging and phone calls are still the
most common communication methods, which suffer from limited availability,
information loss, and process inefficiencies. One promising solution to
facilitate patient-provider communication is to leverage large language models
(LLMs) with their powerful natural conversation and summarization capability.
However, there is a limited understanding of LLMs' role during the
communication. We first conducted two interview studies with both older adults
(N=10) and healthcare providers (N=9) to understand their needs and
opportunities for LLMs in patient-provider asynchronous communication. Based on
the insights, we built an LLM-powered communication system, Talk2Care, and
designed interactive components for both groups: (1) For older adults, we
leveraged the convenience and accessibility of voice assistants (VAs) and built
an LLM-powered VA interface for effective information collection. (2) For
health providers, we built an LLM-based dashboard to summarize and present
important health information based on older adults' conversations with the VA.
We further conducted two user studies with older adults and providers to
evaluate the usability of the system. The results showed that Talk2Care could
facilitate the communication process, enrich the health information collected
from older adults, and considerably save providers' efforts and time. We
envision our work as an initial exploration of LLMs' capability in the
intersection of healthcare and interpersonal communication.Comment: Under submission to CHI202
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Advances in large language models (LLMs) have empowered a variety of
applications. However, there is still a significant gap in research when it
comes to understanding and enhancing the capabilities of LLMs in the field of
mental health. In this work, we present the first comprehensive evaluation of
multiple LLMs, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4, on
various mental health prediction tasks via online text data. We conduct a broad
range of experiments, covering zero-shot prompting, few-shot prompting, and
instruction fine-tuning. The results indicate a promising yet limited
performance of LLMs with zero-shot and few-shot prompt designs for the mental
health tasks. More importantly, our experiments show that instruction
finetuning can significantly boost the performance of LLMs for all tasks
simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5,
outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9%
on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%.
They further perform on par with the state-of-the-art task-specific language
model. We also conduct an exploratory case study on LLMs' capability on the
mental health reasoning tasks, illustrating the promising capability of certain
models such as GPT-4. We summarize our findings into a set of action guidelines
for potential methods to enhance LLMs' capability for mental health tasks.
Meanwhile, we also emphasize the important limitations before achieving
deployability in real-world mental health settings, such as known racial and
gender bias. We highlight the important ethical risks accompanying this line of
research
MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
Problematic smartphone use negatively affects physical and mental health.
Despite the wide range of prior research, existing persuasive techniques are
not flexible enough to provide dynamic persuasion content based on users'
physical contexts and mental states. We first conduct a Wizard-of-Oz study
(N=12) and an interview study (N=10) to summarize the mental states behind
problematic smartphone use: boredom, stress, and inertia. This informs our
design of four persuasion strategies: understanding, comforting, evoking, and
scaffolding habits. We leverage large language models (LLMs) to enable the
automatic and dynamic generation of effective persuasion content. We develop
MindShift, a novel LLM-powered problematic smartphone use intervention
technique. MindShift takes users' in-the-moment physical contexts, mental
states, app usage behaviors, users' goals & habits as input, and generates
high-quality and flexible persuasive content with appropriate persuasion
strategies. We conduct a 5-week field experiment (N=25) to compare MindShift
with baseline techniques. The results show that MindShift significantly
improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use
frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone
addiction scale scores and a rise in self-efficacy. Our study sheds light on
the potential of leveraging LLMs for context-aware persuasion in other behavior
change domains
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Today's AI systems for medical decision support often succeed on benchmark
datasets in research papers but fail in real-world deployment. This work
focuses on the decision making of sepsis, an acute life-threatening systematic
infection that requires an early diagnosis with high uncertainty from the
clinician. Our aim is to explore the design requirements for AI systems that
can support clinical experts in making better decisions for the early diagnosis
of sepsis. The study begins with a formative study investigating why clinical
experts abandon an existing AI-powered Sepsis predictive module in their
electrical health record (EHR) system. We argue that a human-centered AI system
needs to support human experts in the intermediate stages of a medical
decision-making process (e.g., generating hypotheses or gathering data),
instead of focusing only on the final decision. Therefore, we build SepsisLab
based on a state-of-the-art AI algorithm and extend it to predict the future
projection of sepsis development, visualize the prediction uncertainty, and
propose actionable suggestions (i.e., which additional laboratory tests can be
collected) to reduce such uncertainty. Through heuristic evaluation with six
clinicians using our prototype system, we demonstrate that SepsisLab enables a
promising human-AI collaboration paradigm for the future of AI-assisted sepsis
diagnosis and other high-stakes medical decision making.Comment: Under submission to CHI202