WELCOME TO DATA AND SOCIETY

We live in a world where data radically changes how we and society function. An abundance of data is produced in every sector. First, however, the question arises: how do we create this data, for what purpose, and who benefits from it?

We want this course to be responsive to the world and your needs as students. The course schedule is also subject to change.

This course has developed because data fuels many data-driven services and devices powered by artificial intelligence. These new tools and systems profoundly affect how we think of ourselves, relate to one another, organize collective life, and envision desirable futures. So data is the fuel, transforming how we live and society functions. With abundant data being produced in some sectors, the question emerges: how do we use the data, for what purpose, and who is benefiting?

However, data is not something out there, just waiting to be collected, as a "natural resource." One central assumption of this course is "the making of data." We make data by using data collection, analysis, and visualization techniques.

We want to engage you in exploring this construction process and allow them to apply some of these techniques to actual societal situations, possibly controversial situations. How data is created has consequences for society because what is not counted in the data remains invisible (https://www.data4sdgs.org/news/five-ways-end-invisibility-marginalized-people-data).

We want to inform you that not everything and everyone is counted and represented in data. Data is not inclusive, and there are large numbers of human beings who are invisible because they are marginalized in data. This is why two of our learning outcomes are about data biases, their social and cultural consequences, and how data is counted or not counted about gender, class, and ethnicity. For this reason, this course is sustainability-related, as it meets the sustainability criterion of Human Rights and Social Equity.

Many people have already discussed ethical data and data justice. In this course, we view data ethics and data justice as more than just philosophical or technical matters. We believe these topics should also be considered within the broader context of politics, society, culture, technology, and the economy. Additionally, these considerations about data ethics and data justice have implications for machine learning. One aspect to address bias is to use datasets that do not perpetuate specific societal structures, such as racism and sexism.

Drawing from Science and Technology Studies and Critical Data Studies, you will be introduced to the concepts to understand the constructed nature of data. You will develop the skills to connect data construction as a highly technological practice to broad social questions of structural inequalities, evidence, and knowledge. You will examine how datafication produced by digital technologies creates and structures society and maintains specific asymmetries and power relations.

This course is designed for students with or without technical backgrounds who want to gain a deeper understanding of the social and political impacts of data-driven societies today.

Data Justice

"Data justice is a recent discipline that tries to study the relationship between datafication and social justice, highlighting the politics and impacts of data-driven processes and big data. Specifically, data justice can be defined as fairness in the way people are made visible, represented and treated as a result of their production of digital data" (Taylor, 2017).

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Discussing data justice suggests a need to position data in a way that engages more explicitly with questions of power, politics, inclusion, and interests, as well as established notions of ethics, autonomy, trust, accountability, governance, and citizenship (Dencik et al., 2019). From a data justice perspective, it's important to address the following questions:

1) How is data bringing attention to the concerns of different individuals and groups, and how are these concerns represented?

2) In what ways is the involvement with data ensuring that people have a share in its benefits, and also have the choice and autonomy to determine how it is used?

3) How can people challenge and prevent biases and discrimination in data-driven processes (Taylor, 2017).

What makes this course different from others in the data science field

This course does not cover data management or data curation. Nor is it a course in data science. Nor are we teaching research methods.

Course Origins

This course draws on the reading list and syllabus from a prior data inquiry course. The most direct inspiration comes from the data inquiry course developed by researchers at the Public Data Lab.

Approach to Teaching

During most class sessions, there will be a combination of lecture and discussion. The lectures will mainly provide background information to facilitate a deeper analysis of primary and secondary literature. In addition to learning the main principles of the field, we will emphasize critical thinking and reasoning through discussions and presentations.

Hours and Workload

On average, four contact hours of instructor presentation of course materials per week (approx 34 contact hours), + 17 hours of outside work per week.

References:

Dencik, L., Hintz, A., Redden, J., & Treré, E. (2019). Exploring Data Justice: Conceptions, Applications and Directions. Information, Communication & Society, 22(7), 873–881. https://doi.org/10.1080/1369118X.2019.1606268

Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2). https://doi.org/10.1177/205395171773633

Last edit: 28 August 2024