Just Data? Re-imagining Inequality Research

Course Description

Data science is having a moment, with researchers across academia, industry and government rushing to capitalize on new information sources and the increasing quantification of social life. But the ascendance of algorithms and artificial intelligence also requires renewed attention to data provenance and quality: as the old programming saying goes, “garbage in, garbage out.” In this class, we will take a critical look at sources of social and demographic data, as well as common assumptions that go into collecting and analyzing them, with the aim of becoming more informed consumers and users of social statistics.

Our particular focus is on inequality research—research that seeks to identify differences, disparities or inequities between groups of people based on factors like race or ethnicity, sex or gender, or sexuality. Through course readings and case studies we will consider the promise and pitfalls of making these categorical comparisons: although researchers often do this work because they want to challenge inequity or alleviate inequality such research also can do harm (often inadvertently), depending on how it is executed and interpreted. We will explore these challenges across the various phases of research: from data collection and analysis to data visualization and communicating results through the media. We will also consider a range of applications: from practices of race correction in medicine to counting families and households in the census. Through course assignments and activities, we will practice spotting common problems and proposing solutions. By the end of the quarter, students will be better positioned to both critique existing research and conduct more responsible analyses of their own.