Algorithms That Make You Think
Fourth Annual Virginia Tech Workshop on the Future of Human-Computer Interaction
April 11-12, 2019
For the Program, Registration and Reading Group, please see: http://wordpress.cs.vt.edu/algorithmsworkshop
Algorithms play an increasingly important role in shaping many aspects of our daily lives. Researchers across a wide variety of disciplines including human-computer interaction, communication, law, and computer science have debated over the ways in which algorithms govern and keep the human out of the decision making process.
- What if human-algorithm communication were different?
- What if we purposely involve humans in the algorithmic decision making pipeline?
- What if we support human agency and traits in building and sustaining algorithmic data and decision systems?
- How can the inherent values of human critical thinking and creativity be incorporated into algorithms?
- How can we reconcile diverse perspectives and points of view about human-algorithm communication?
- How can we design effective human-machine collaborations for critical analysis?
The Virginia Tech Center for Human-Computer Interaction is hosting a two-day workshop, to be held April-11-12, 2019 in Blacksburg, Virginia, to find answers to these key questions pertaining to human-algorithmic interactions. The workshop aims to bring in scholarly expertise from a wide range of disciplines, including human-computer interaction, communication, law, and computer science. Therefore, a driving force behind this workshop is to encourage increased dialogue among researchers from different domains with intersecting interests in human-algorithm communication.
Workshop outcomes may include system designs, best practices for ethical and responsible algorithm development, research and policy proposals, and publications and recommendations for building this area of research and practice.
Does human autonomy have a place in discussions of algorithmic fairness?
Research in the area of algorithmic ‘fairness’ often leans toward narrower definitions of fairness that can be subject to formalism and implementation. This is likely influenced by the disciplinary orientation of participating scholars. Such discussions often seek to demonstrate or prove fairness as itself an algorithmic question, one rooted in questions of distribution. This is closer to ways of thinking about fairness within economics, but more distant from legal views or activist campaigns that align fairness more broadly with justice. In this talk, I will describe my own recent research to slightly expand the conversation outward beyond an inner circle of algorithmic fairness scholars, to include the experiences of people subject to algorithmic classification and decision-making. Using Twitter data, and specifically tweets about the “twitter algorithm,” I consider a wide range of concerns and desires Twitter users express. I find a concern with fairness (narrowly construed) is present, particularly in the ways users complain about the algorithms bias against particular groups. However, I find in collected tweet data another important category of concerns, reflecting a desire to enact more control over the algorithm. These tweets center consideration for human autonomy in the face of automation. In this talk, I will consider how questions of autonomy are implied in the framing of fairness, but also why they often end up being relegated to an ancillary concern. I will highlight some ways forward that respond to appeals for autonomy by those subject to algorithmic decision-making.
Jenna Burrell is an Associate Professor in the School of Information at UC Berkeley. She is the co-director of the Algorithmic Fairness and Opacity Working Group. Her first book Invisible Users: Youth in the Internet Cafes of Urban Ghana (The MIT Press) came out in May 2012. She is currently working on a second book about rural communities that host critical Internet infrastructure such as fiber optic cables and data centers. She has a PhD in Sociology from the London School of Economics. Her research focuses on how marginalized communities adapt digital technologies to meet their needs and to pursue their goals and ideals.
Human-Centered Data Science: Visual Interfaces for Making Sense of Data and Machine Learning
The ever growing volume of digital information has led to promises of a golden age for data analysis. While access to such data is paramount, access alone is ultimately insufficient without understanding the data’s patterns, identifying its outliers, and discovering its gaps. Data scientists often struggle to discover such insights in large and complex datasets without relying on automated machine learning approaches. However, purely automated techniques limit scientists’ abilities to explore data creatively or exploit their own domain expertise.
My research focuses on augmenting human intelligence by designing, building, deploying, and studying human-centered approaches to data science. I characterize my research as Human-Centered Data Science where I combine techniques from statistics, machine learning, and data visualization to empower data scientists throughout their analytical workflow. My talk will focus on three themes: designing tools for interactive and visual data science, providing data scientists with information and insights from interpretable machine learning and predictive modeling, and directly impacting a challenging and important domain with data-driven healthcare.
Adam Perer is an Assistant Research Professor at Carnegie Mellon University, where he is a member of the Human-Computer Interaction Institute. His research integrates data visualization and machine learning techniques to create visual interactive systems to help users make sense out of big data. Lately, his research focuses on human-centered data science and extracting insights from clinical data to support data-driven medicine. This work has been published at premier venues in visualization, human-computer interaction, and medical informatics. He was previously a Research Scientist at IBM Research. He holds a Ph.D. in Computer Science from the University of Maryland, College Park. More information about Adam's research is available at http://perer.org