Sponsors & Mentors: Building Your Academic Professional Network

Speakers: A.J. Brush (Microsoft) & Kelly Shaw (Williams College)

What’s a mentor? What’s a sponsor? What’s the difference? Do you need both in your career? Come learn the answers to these questions as well as practical tips for identifying sponsors and mentors and building your professional network.

I’m a Student Again: Heading to Graduate School after a Break

Speakers: Dilma Da Silva (Texas A&M University) & Monica Anderson (University of Alabama)

Considering graduate school after some time working? Already back in school? This session will cover how to position yourself to apply to graduate school, the differences between professional Master’s and Ph.D. programs, and tips for being successful in graduate school. We’ll have plenty of time for questions, so bring yours.

Effective Teaching Tactics: In-person, Hybrid, Virtual – Wow!

Speakers: Susan Rodger (Duke University) & Colleen Lewis (University of Illinois, Urbana Champaign)

How to plan, manage, run and assess a course; dealing effectively with large enrollments; managing your TAs (managing down). What to do about low teaching evaluations? Strategies for effective online teaching; effective use of technology in the classroom.

ACM Award-Winning Research in HCI

A.J. Brush (Microsoft): Moderator

Amy Rechkemmer (Purdue University) — ACM Award Winning Research in HCI: When Confidence Meets Accuracy: Exploring the Effects of Multiple Performance Indicators on Trust in Machine Learning Models

Previous research shows that laypeople’s trust in a machine learning model can be affected by both performance measurements of the model on the aggregate level and performance estimates on individual predictions. However, it is unclear how people would trust the model when multiple performance indicators are presented at the same time. We conduct an exploratory human-subject experiment to answer this question. We find that while the level of model confidence significantly affects people’s belief in model accuracy, both the model’s stated and observed accuracy generally have a larger impact on people’s willingness to follow the model’s predictions as well as their self-reported levels of trust in the model, especially after observing the model’s performance in practice. We hope the empirical evidence reported in this work could open doors to further studies to advance understanding of how people perceive, process, and react to performance-related information of machine learning.

Emily Tseng (Conell University) — ACM Award-Winning Research in HCI: Care Infrastructures for Digital Security and Privacy in Intimate Partner Violence

Survivors of intimate partner violence (IPV) face complex threats to their digital privacy and security. Prior work has established protocols for directly helping them mitigate these harms; however, there remains a need for flexible and pluralistic systems that can support survivors’ long-term needs. This paper describes the design and development of sociotechnical infrastructure that incorporates feminist notions of care to connect IPV survivors experiencing technology abuse with volunteer computer security consultants. We present findings from a mixed methods study that draws on data from an 8-month, real-world deployment, as well as interviews with 7 volunteer technology consultants and 18 IPV professionals. Our findings illuminate emergent challenges in safely and adaptively providing computer security advice as care. We discuss implications of these findings for feminist approaches to computer security and privacy, and provide broader lessons for interventions that aim to directly assist at-risk and marginalized people experiencing digital insecurity.

Keke Wu (University of North Carolina) — ACM Award Winning Research in HCI: Understanding Data Accessibility for People with Intellectual and Developmental Disabilities

Using visualization requires people to read abstract visual imagery, estimate statistics, and retain information. However, people with Intellectual and Developmental Disabilities (IDD) often process information differently, which may complicate connecting abstract visual information to real-world quantities. This population has traditionally been excluded from visualization design, and often has limited access to data related to their well being. We explore how visualizations may better serve this population. We identify three visualization design elements that may improve data accessibility: chart type, chart embellishment, and data continuity. We evaluate these elements with populations both with and without IDD, measuring accuracy and efficiency in a web-based online experiment with time series and proportion data. Our study identifies performance patterns and subjective preferences for people with IDD when reading common visualizations. These findings suggest possible solutions that may break the cognitive barriers caused by conventional design guidelines.