Data is one of the most valuable commodities and resources of the twenty-first century. Data is used to describe and classify cultural objects, to track consumption and movement, to make decisions and predict activity. This data, often referred to as “Big Data” is constantly evolving, expanding, and changing, increasingly defining the spaces and systems that surround us. Humanities and social science researchers have turned increasingly to data to learn more about cultural and social systems, to discover how they form, evolve, and develop over time. But this exploration often leads to discovery of injustice, discrimination, and bias in these systems. “Data Feminism”, a term coined by Catherine D’Ignazio and Lauren Klein, offers a critical framework for thinking about data science and data ethics that is informed by intersectional feminist thought.
In the Spring-Summer 2024 session, students in Big Data, Culture and Society surveyed the history of data through the cultural, social, and ethical dimensions of the topic. We discussed issues surrounding classification; explored the collection, use, and misuse of data in a variety of social and cultural contexts; and examined how structural inequities built into foundational classification structures permeate our world today in the algorithms underpinning search engines, digital streaming services, and more.
Throughout the term, we considered how today’s systems and cultural policy perpetuate systemic inequities and injustice, but also consider how Big Data and these systems can be used for advocacy and social good.
This exhibit shares the result of some student projects.
- The Use of Artificial Intelligence in Creative Writing and Fanfiction by Leigh Ingram
- Baking Bias into Targeted Ads by Xinlei Zhou
- Challenges in AI Education – Changing the Framework by Michael Wells (coming soon!)
- My doctor said…Treatment of Women in the Canadian Medical System by Margaret Rose (coming soon!)