Quantified Lives: Data, Bias, and the Cost of Categorization

Author

Colin F. Geraghty, The Graduate Center, City University of New YorkFollow

Date of Degree

2-2025

Document Type

Capstone Project

Degree Name

M.S.

Program

Data Analysis & Visualization

Advisor

Howard T. Everson

Subject Categories

Data Science | Digital Humanities | Evolution | Philosophy of Science | Quantitative Psychology | Race and Ethnicity | Race, Ethnicity and Post-Colonial Studies

Keywords

Statistical Categorization, Eugenics, Data Visualization, Bias in Metrics, Ethics in Data Science, Categorization Frameworks

Abstract

This project critically examines how statistical methods and data visualization have historically been used to categorize, rank, and control human populations. By exploring the works of key figures like Francis Galton, Karl Pearson, and R.A. Fisher, it traces the evolution of categorization frameworks from their eugenic roots to modern applications in artificial intelligence and global metrics like the World Happiness Report. Through interactive visualizations and historical analysis, the project invites users to question the legitimacy of these frameworks and reflect on their impact on society. At its core, this work emphasizes the need to move beyond rigid classification systems, challenging the assumptions that underpin them, and advocating for more ethical and thoughtful applications of data.

Comments

Online component: https://cgeraghty1.github.io/capstone/

Recommended Citation

Geraghty, Colin F., "Quantified Lives: Data, Bias, and the Cost of Categorization" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/6167