Image Databases for Facial Analysis Coded for Race and Gender Features

This document consists of the corpus of image databases examined for race and gender information as published in: <em>Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, and Jed R. Brubaker. 2020. How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis. Proc. ACM Hum.-Comput. CSCW. </em> This code book includes: 1. Whether race/gender is present implicitly (as descriptive, but not annotated) or explicitly (annotated/labeled). 2. What categories or descriptions of race/gender are used. 3. Whether those categories/descriptions use underlying source material to justify or motivate their descriptions of race/gender. 4. Whether explicitly annotated databases describe the process of annotating race/gender.