Jina Suh

University of Washington | UW · Department of Human Centered Design and Engineering

As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety seems paramount. ``Red-teaming'' has quickly become the primary approach to test AI models--prioritized by AI companies, and enshrined in AI policy and regulation. Members of red teams act as adversaries, probing AI syste...

Recent gain in popularity of AI conversational agents has led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences. Additionally, psychologica...

Behind the scenes of maintaining the safety of technology products from harmful and illegal digital content lies unrecognized human labor. The recent rise in the use of generative AI technologies and the accelerating demands to meet responsible AI (RAI) aims necessitates an increased focus on the labor behind such efforts in the age of AI. This stu...

Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics, influencing beliefs about work and developers' roles in the workplace. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter...

Client-Service Representatives (CSRs) are vital to organizations. Frequent interactions with disgruntled clients, however, disrupt their mental well-being. To help CSRs regulate their emotions while interacting with uncivil clients, we designed Pro-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use. Our comparative ana...

A critical factor in the success of many decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role of system-user interaction in developing personalized systems. This paper introduces a novel app...

The global COVID-19 pandemic has spurred on new collaborations across borders, and emphasized the importance of supporting wellbeing in the workplace, whether that workplace is hybrid, remote, or in-person. Work in CSCW, HCI, and organizational psychology has explored how people come to understand their wellbeing at work, and the role of identity,...

Work-nonwork balance is an important aspect of workplace well-being with associations to improved physical and mental health, job performance, and quality of life. However, realizing work-nonwork balance goals is challenging due to competing demands and limited resources within organizational and interpersonal contexts. These challenges are compoun...

Background Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITA...

BACKGROUND Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITA...

Re-finding information is an essential activity, however, it can be difficult when people struggle to express what they are looking for. Through a need-finding survey, we first seek opportunities for improving re-finding experiences, and explore one of these opportunities by implementing the FoundWright system. The system leverages recent advances...

The COVID-19 pandemic has stimulated important changes in online information access as digital engagement became necessary to meet the demand for health, economic, and educational resources. Our analysis of 55 billion everyday web search interactions during the pandemic across 25,150 US ZIP codes reveals that the extent to which different communiti...

Workplace stress has been increasing in recent decades and has worsened by the unique demands imposed by COVID-19 and the new remote/hybrid work settings. High-stress working conditions can be detrimental to the health and wellness of workers and can lead to significant business costs in terms of productivity loss and medical expenses. An essential...

BACKGROUND Emotion dysregulation is key to the development and maintenance of chronic pain, feeding into a cycle of worsening pain and disability. Dialectical behavioural therapy (DBT), an approach which targets emotional processes through skills training to induce effective emotion regulation, could help people manage and mitigate the emotional an...

Background: Emotion dysregulation is key to the development and maintenance of chronic pain, feeding into a cycle of worsening pain and disability. Dialectical behavioral therapy (DBT), an evidence-based treatment for complex transdiagnostic conditions presenting with high emotion dysregulation, may be beneficial to manage and mitigate the emotion...

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser extension for accelerating people’s online research experience. The two primary sources of nove...

Emotion dysregulation frequently co-occurs with chronic pain, which in turn leads to heightened emotional and physical suffering. This cycle of association has prompted a recommendation for psychological treatment of chronic pain to target mechanisms for emotion regulation. The current trial addressed this need by investigating a new internet-deliv...

The COVID-19 pandemic has stimulated a staggering increase in online information access ( 1, 2 ), but the extent to which different communities of internet users enlist digital resources to meet everyday needs varies ( 2-4 ). We analyze 55 billion everyday web search interactions across 25,150 US ZIP codes and demonstrate that there were disparate...

How might computing support us in becoming our better, more emotionally resilient selves? We explore this in an interview with the team from Microsoft Research's Human Understanding and Empathy group.

Introduction Difficulties in emotional regulation are key to the development and maintenance of chronic pain. Recent evidence shows internet-delivered dialectic behaviour therapy (iDBT) skills training can reduce emotional dysregulation and pain intensity. However, further studies are needed to provide more definitive evidence regarding the efficac...

Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology. However, pandemic-related policy decisions cannot be made on health information alone but need to take into account the broader impacts on people and their needs. Quantifying human needs across the population is challenging as it requires high...

Mobile mental health interventions have the potential to reduce barriers and increase engagement in psychotherapy. However, most current tools fail to meet evidence-based principles. In this paper, we describe data-driven design implications for translating evidence-based interventions into mobile apps. To develop these design implications, we anal...

Modern systems can augment people’s capabilities by using machine-learned models to surface intelligent behaviors. Unfortunately, building these models remains challenging and beyond the reach of non-machine learning experts. We describe interactive machine teaching (IMT) and its potential to simplify the creation of machine-learned models. One of...

When building a classifier in interactive machine learning (iML), human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help discover or refine concepts for which the current classifier has no corresponding features (i.e.,...

Advances in artificial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing a...

Current Machine Learning (ML) models can make predictions that are as good as or better than those made by people. The rapid adoption of this technology puts it at the forefront of systems that impact the lives of many, yet the consequences of this adoption are not fully understood. Therefore, work at the intersection of people's needs and ML syste...

Machine learning (ML) has become increasingly influential to human society, yet the primary advancements and applications of ML are driven by research in only a few computational disciplines. Even applications that affect or analyze human behaviors and social structures are often developed with limited input from experts outside of computational fi...

Machine learning (ML) promises data-driven insights and solutions for people from all walks of life, but the skill of crafting these solutions is possessed by only a few. Emerging research addresses this issue by creating ML tools that are easy and accessible to people who are not formally trained in ML ("non-experts"). This work investigated how n...

When building a classifier in interactive machine learning, human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help discover or refine concepts for which the current classifier has no corresponding features (i.e., it has...

The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them. We believe that in...

In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. These features recognize document contexts instead of n-grams. We describe a principled methodology to solicit dictionary features from a teacher, and present results showing...

Performance analysis is critical in applied machine learning because it influences the models practitioners produce. Current performance analysis tools suffer from issues including obscuring important characteristics of model behavior and dissociating performance from data. In this work, we present Squares, a performance visualization for multiclas...

Model building in machine learning is an iterative process. The performance analysis and debugging step typically involves a disruptive cognitive switch from model building to error analysis, discouraging an informed approach to model building. We present ModelTracker, an interactive visualization that subsumes information contained in numerous tra...

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. W...