Modeling Ethics: Approaches to Data Creep in Higher Education

Area of Science:

• Sociology of Science and Technology
• Informatics and Data Science Ethics

Background:

• Big data collection is widespread, raising ethical concerns.
• Data creep, the repurposing of collected data, is a significant ethical challenge.
• Existing institutional review board (IRB) guidelines offer limited guidance for big data research.

Purpose of the Study:

• To explore the complexities of big data ethics, focusing on data creep.
• To compare institutional models of ethics with actual practices and intentions.
• To propose a new framework for data ethics in institutional contexts.

Main Methods:

• Ethnographic research at a U.S. public university.
• Interviews and participant observation with administrators, data scientists, developers, and students.
• Analysis of a predictive model using student data to anticipate academic success.

Main Results:

• Student consent for data collection is often unclear due to data creep.
• Institutional ethics procedures may not fully address the realities of big data usage.
• Everyday enactments of data ethics reveal discrepancies with formal policies.

Conclusions:

• Current models of institutional ethics are insufficient for big data.
• Rethinking data ethics requires considering justice and refusal.
• A new ethical framework is needed for responsible big data research and application.