NEW YORK UNIVERSITY (2019)
Professor Anne L. Washington
“Ethics of Data Science” explores the societal consequences of extracting inferences about human behavior from digital abstractions. Historical and theoretical perspectives will serve as the foundation for exploring contemporary concerns about data science, predictive analytics, artificial intelligence, and other data-driven tools. The course is designed to build students’ ethical imaginations and data literacy using both inductive and deductive reasoning. The course provides practical guidance on how to uncover ethical weaknesses as well as construct principled data-driven projects.
Data sets store the words, movements, and purchases of billions of people. Data scientists abstract this daily human activity into digital representations that are designed to compare and contrast us to other people. Comparing an individual to a larger population historically has always had moral, legal, and social implications. What is different today is that digital comparisons immediately cycle back to impact our lived experience of friendship, navigation, government, health care, community, politics, employment, transportation, or commerce. Because data are rarely created for one-time use, downstream ethical implications may accumulate, especially when comparing across jurisdictional or cultural contexts. Data science that shapes society requires expert technical abilities as well as careful critical thinking skills in quantitative reasoning.
Assignments heighten an awareness of competing interests in ethical dilemmas. Students will explore ethical topics through their own cultural values as well as learning to reflect on the logic of alternative perspectives. By completing case studies, discussions, problem sets, and essays, students will have an opportunity to consider both the technical and theoretical aspects of ethical data science. Students will be encouraged to connect readings from in-depth reporting, technology ethnography, and primary sources to relevant current events. Students in this seminar will learn how technical choices in statistics and algorithms can impact society.