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 ascendence 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 take a critical look at the sources of social and demographic data, as well as the 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. Through course readings and case studies we will consider the promise and pitfalls of making group comparisons: although researchers often do this work because they want to challenge inequity or alleviate inequality, depending on how it is executed and interpreted, such research also can do harm (inadvertently or otherwise). We will explore these challenges across 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.
4 units · Letter or Credit/No Credit · GER: WAY-EDP, WAY-SI
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 ascendence 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 take a critical look at the sources of social and demographic data, as well as the 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. Through course readings and case studies we will consider the promise and pitfalls of making group comparisons: although researchers often do this work because they want to challenge inequity or alleviate inequality, depending on how it is executed and interpreted, such research also can do harm (inadvertently or otherwise). We will explore these challenges across 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.
Offered in Spring 2027 at Stanford University.