LIS-S 302 Data and Society
3 credits
- Prerequisite(s): None
- Delivery: Online
- Semesters offered: Fall (Check the schedule to confirm.)
Description
This course reviews big and small data practices in research, education, business, government, and nonprofits, while critically examining the role of data in society. Using case studies, students will address ethical questions related to fairness, discrimination, power, and privilege. Topics include machine learning, black-box algorithms, wearable technology, data justice, and data activism, among others.
This course provides conceptual and methodological toolkits developed within the broad field of social studies of science, including critical data studies, data ethics, and the history, philosophy, and science and technology studies. The questions we grapple with include: How do data infrastructures and institutions work? What does it mean to critically approach and assess data creation, access, and utilization? How does data reproduce or reinforce socioeconomic inequalities? How can we promote good data practices and data justice?
Program Learning Outcomes Supported
Instructors map their courses to specific Data Science Program Learning Outcomes (PLOs). Mapped program goals drive the design of each course and what students can expect to generally learn.
- A2: Data Literacy - Recognize that data can have value and play a key role in society by providing opportunities to expand knowledge, to innovate, and to influence.
- A4: Data Literacy - Assess values with respect to the use of data technologies.
- B6: Data Science - Understand data science concepts, techniques, and tools to support big data analytics.
- C5: Information Science - Understand critical issues associated with the storage, backup, and security of data.
- C6: Information Science - Analyze data policies to compare possible outcomes.
- D1: Data Ethics - Understand the relation between data, ethics, and society.
- D2: Data Ethics - Identify and understand the social, political, ethical, and legal aspects of data creation, access, ownership, service, and communication.
- D3: Data Ethics - Develop substantive arguments using ethical reasoning to suggest improvements to data-driven systems and practices.
- D4: Data Ethics - Differentiate between surveillance systems that promote and inhibit values.
- E1: Other Topics - Design, conduct, and write up results of research.
Learning Outcomes
- Understand datafication processes and critically evaluate hyperbolic claims regarding the power of Big Data.
- Communicate theories and concepts in the social sciences to gain historical, political-economic, and cultural insights into data flow and algorithmic systems.
- Explain how socio-technical systems of data creation, analysis, and interpretation perpetuate and reinforce inequalities.
- Critically reflect on emerging issues and technologies related to data justice and ethics and develop well-informed claims or decisions.
- Evaluate institutional and grassroots efforts aimed at holding algorithmic systems and data practices accountable and promoting equity.
Profiles of Learning for Undergraduate Success (PLUS) Alignment
Instructors align their courses with the Profiles of Learning for Undergraduate Success. The profiles provide students various opportunities to deepen disciplinary understanding, participate in engaged learning, and refine what it means to be a well-rounded, well-educated person prepared for lifelong learning and success.
- P2.1 Problem Solver – Think critically
- P2.3 Problem Solver – Analyzes, synthesizes, and evaluates
- P3.1 Innovator – Investigates
- P4.4 Community Contributor – Anticipates consequences
Course Overview
Module 1: Introduction to the Course
- Course Basics
Module 2: Datafication, Data in Context, Data in Society
- Conceptual framings developed through interdisciplinary efforts to critically evaluate the multiple roles of data in society
Module 3: Do Machines 'Learn'? Do they 'Think'?
- What is artificial intelligence? What is machine learning? How do they work?
- The algorithmic opacity and the value of transparency
Module 4: Algorithmic Curation - Social Media Platforms
- How algorithmically curated contents shape user experiences
- Algorithms for personalization and their impact on democracy
Module 5: Algorithmic Discrimination
- How data mining processes and classification system work:
- How algorithmically driven predictions can reinforce existing bias and reproduce inequalities
Module 6: Algorithmic Self and Everyday Life
- Wearable technologies, self-tracking devices, and the quantified self
- Algorithmic assistance for personal connection and social relationship
Module 7: Data Controversy – Research
- Analyze current issues on data/algorithmic controversy
Module 8: Data Controversy – Discussion
- Student-led discussions
Module 9: Data and the Future of Cities
- The datafication of urban lives and environments
- Benefits and harms to use data and algorithms in city management
Module 10: Data and the Future of Work
- Platform-based economy and labor
- How data-driven economy changed the nature and geographies of labor
Module 11: Data Ethics and Data Justice(s)
- Concepts and forms of data ethics and justice
- Applying data ethics and justice to assess data-driven systems and decisions
Module 12: Open Data and Citizen Data Initiatives
- What is open data? Promises and concerns
- Citizen data initiatives and intermediaries
Module 13: Data Activism and Alternative Imaginaries
- What does opportunities and limitations data create for social change?
- Alternative imaginaries/knowledge/practices of data in non-western countries
Module 14: Data Advocacy – Research
- Analyze a data activist group and their activities
Module 15: Data Advocacy – Discussion
- Student-led discussions
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.