Matthew Hong: Personalizing Health Management Through Human-Centered Data Augmentation
During complex chronic treatment, adolescent patients (ages 10-19) must communicate all illness needs to the care team so they can access relevant health resources when most needed. This communication is challenging because patients, family caregivers and clinicians have unmatched experiences, conceptions and linguistic representations of indicators of health. Most importantly, patients lack the means to capture and represent their felt illness experience. My colleagues and I addressed these challenges by advancing personalized computing technology and human-centric methods that inform collaborative approaches for managing personal health data. In this talk, I will describe how technology can be designed to effectively scaffold patients’ gradual participation in managing their illness. I draw from Health Informatics, Participatory Design, and Human-Computer Interaction to show how we can augment clinically-generated, and patient-generated data in ways that cater to personal health needs. I will discuss how human-centered data-augmentation can help designers create intelligent systems to improve chronic care for pediatric patients.
Lara Martin: Understanding the Technological and Experiential Requirements of Improvisational Storytelling Agents
Although we are currently riding a technological wave of personal assistants, many of these agents still struggle to communicate appropriately. Humans are natural storytellers, so it would be fitting if artificial intelligence could tell stories as well. Automated story generation is an area of AI research that aims to create agents that tell “good” stories. Previous story generation systems use planning to create new stories, but these systems require a vast amount of knowledge engineering. The stories created by these systems are coherent, but only a finite set of stories can be generated. In contrast, very large language models have recently made the headlines in the natural language processing community. Though impressive on the surface, these models begin to lose coherence over time. My research looks at various techniques of automated story generation, focusing on the perceived creativity of the generated stories. Here, I define a creative product as one that is both novel and useful. In my dissertation, I theorize that a jointly probabilistic and causal model will provide more creative stories for readers of stories generated from an improvisational storytelling system than solely probabilistic or causal models.
Emily Wall: Mitigating Implicit Human Bias in Visual Analytics
Implicit bias is a term used to describe the way that our culture, experiences, and stereotypes can unconsciously impact our attitudes and decision making. Such biases, like racial or gender bias, can impact decision making in critical ways, propagating long-standing institutional and systemic biases. However, as decision making is increasingly taking place with the aid of data-driven visual representations (including interactive visualization tools like Tableau, among others), we are afforded a new opportunity with respect to the detection and mitigation of implicit biases. In this talk, I describe (1) how user interactions with data can be used to approximate implicit biases and (2) how visualization systems can be designed to make implicit biases more explicit by increasing awareness. By creating systems that promote real-time awareness of bias, people can reflect on their behavior and decision making and ultimately engage in a less-biased decision making process.
Matthew Hong is a PhD candidate in Human-Centered Computing in Interactive Computing. His research lies at the intersection of Human-Computer Interaction and Health Informatics, and focuses on supporting pediatric patients’ management of complex chronic conditions through the design and deployment of human-centered health technologies. He has published in leading conference proceedings and journals, including the ACM Conference on Human Factors in Computing Systems (ACM SIGCHI) and the Journal of American Medical Informatics Association (JAMIA).
Lara Martin is a Human-Centered Computing PhD Candidate in the College of Computing at Georgia Tech. Her work resides in the field of Human-Centered Artificial Intelligence with a focus on natural language applications. Lara is currently working on automated story generation, but her previous work includes speech processing and analyzing online communities. Lara earned a Masters of Language Technologies from Carnegie Mellon University and a BS in Computer Science & Linguistics from Rutgers University—New Brunswick. In addition to winning the Foley Scholar Award, she recently received the Best Doctoral Consortium Presentation award at the 2019 ACM Richard Tapia Celebration of Diversity in Computing Conference.
Emily Wall is a 2019 Foley Scholar and Computer Science PhD candidate in the School of Interactive Computing, where she is advised by Dr. Alex Endert. Her research interests lie at the intersection of cognitive science and data visualization. Particularly, her dissertation has focused on increasing awareness of unconscious and implicit human biases via the design and evaluation of (1) computational approaches to quantify bias from user interaction and (2) interfaces to support visual data analysis. Her research has been supported by the Siemens FutureMaker Fellowship and the National Physical Science Consortium Fellowship, among others.