INF 385T: Special Topics in Information Science: Responsible Data Management

Spring Term 2024
Mode: In-Person
Instructor
Hanlin Li
Program: MSIS/PhD
Unique ID
27754
Day Start End Building Room
  • Wednesday
  • 12:00 PM
  • 3:00 PM
  • UTA
  • 1.208

Catalog Description

Explore common data collection, documentation, management, and sharing practices involved in the technology sector and research and information institutions. Students will examine the human, social, and ethical impact of these practices and work on group projects to design data systems that are centered around broader impact and social responsibilities. 

 

Instructor Description

This course will explore common data collection, management, and sharing practices involved in the technology sector and in research and information institutions. Drawing from library and archival science, computing, and Science and Technology Studies, Responsible AI,it offers a holistic view of data ecosystems and infrastructures. Students will examine the full data lifecycle, from the people who produce data, to the intermediaries who collect and document it, to the consumers who analyze and share it. Discussions center on emergent ethical issues about dataset management related to documentation, provenance, sovereignty, consent, deprecation, etc.
 
Students will also develop projects to review data practices, collect data, make data-driven decisions, and design data-informed systems as information and technology professionals. Projects will be tailored to students’ disciplinary background and no programming background required.  

 

Prerequisites

Graduate standing.

Restrictions

Restricted to graduate students in the School of Information through registration periods 1 and 2. Outside students will be permitted to join our waitlists beginning with registration period 3.

Notes

This course will explore common data collection, management, and sharing practices in information technology and emerging technologies, such as search engines and AI systems. Students will read papers and engage in discussions about the pros and cons of established data practices and learn about the three main components of responsible data management: 1) consent and ownership, 2) privacy and anonymity, and 3) broader impact. Students will also practice how to design ethical data-driven products through group projects as UX designers, researchers, and data scientists. The course will bring in interdisciplinary perspectives with guest speakers from archive science, engineering, and responsible AI, to provide a holistic view of broader data ecosystems and infrastructures. Students will learn the pros and cons of different data collection, management, and sharing practices. Students will gain hands-on experience with designing data-driven products or systems as UX designers, researchers, and data scientists. Students will also be exposed to interdisciplinary research on important ethical considerations about data, e.g. privacy and consent.

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