All data is human: how can we address bias and inequality in data science?

Description

Join leading practitioners to discuss best practices for addressing the bias, inequality and ethical issues that may result from the age of automated data science.

Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques.

Ahead of the release of a new book, written by founders of the field, this event will explore recommended approaches to addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets.

It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods.

This event will also feature a brief case study from Casey Fiesler, University of Colorado, exploring the ethics of scraping data.

Speakers

• Cecilia Aragon, Cecilia Aragon, Professor, Department of Human Centered Design and Engineering, University of Washington
• Shion Guha, Assistant Professor, Faculty of Information, University of Toronto
• Marina Kogan, Assistant Professor, School of Computing, University of Utah
• Michael Muller, Research staff member, IBM Research
• Professor Gina Neff, Executive Director, Minderoo Centre for Technology and Democracy, University of Cambridge
• Casey Fiesler, University of Colorado