Data, Culture, and Justice

Module overview

Data organise our present and shape our future. Those data are never neutral because they are the product of human labour, of choices made by people about what data to record, how to record it, and who is best equipped to do that recording. Drawing on work from intersectional feminism, anti-colonial theory, and infrastructure studies, this module takes a justice-led approach to data as both products and producers of culture. It examines the ways that the datafication of culture has produced predictive systems that police us, structures that define us, and products that simulate us. It explores the connections between historical forms of data production and present day inequities. It discusses the value, purpose, and variety of justice-led approaches to analysing data and culture. And it considers how we might creatively resist, reimagine, and remake the relationship between data, culture, and social justice.

No technical or theoretical knowledge is required to take this module. It is open to all, whether you want to develop a justice-led approach to thinking about the intersections of data and culture, or you want to work with data to apply justice-led thinking to your analysis of culture.

Aims and Objectives

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

You will be able to participate effectively in situations that require group work.
You will be able to act in justice-led ways to the production, use, and reuse of data in culture and wider society.
You will be able to act as an informed citizen in your production and reuse of data.
You will be able to act as an informed citizen in your use of data.
You will be able to act reflexively in your response to injustices amplified by the use of data.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

You will be able to demonstrate knowledge and understanding of effective group work.
You will be able to demonstrate knowledge and understanding of reflexive data practice.
You will be able to demonstrate knowledge and understanding of the role of data in shaping culture and perpetuating injustice.
You will be able to demonstrate knowledge and understanding of the intersections between data production and its cultural uses.
You will be able to demonstrate knowledge and understanding of justice-led practices and how to apply that practice to the production and use of data.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

You will be able to apply your knowledge of what makes for good group work to your wider programme of study.
You will be able to apply your knowledge of justice-led approaches to data to your wider programme of study.
You will be able to apply your knowledge of how knowledge production, datafication, and algorithmic systems intersect to your wider programme of study.
You will be able to apply to your wider programme of study your knowledge of the role of data in shaping culture and perpetuating injustice.
You will be able to apply your reflexive data practice to your wider programme of study.

Learning and Teaching

Teaching and learning methods

Teaching methods:

Sessions divided into topic discussion and supervision of group practical work.
Topic mini-lectures will be pre-recorded for use during independent study time.

Learning activities include:

In-depth analysis of critical texts
Preparatory reading, practical experimentation, and individual study
Individual participation in seminars and group work

Study time
Type	Hours
Independent Study	114
Teaching	36
Total study time	150

Resources & Reading list

Journal Articles

Abeba Birhane (2021). Algorithmic Injustice: A Relational Ethics Approach. Patterns, 2(2).

Temi Odumosu (2020). The Crying Child: On Colonial Archives, Digitization, and Ethics of Care in the Cultural Commons. Current Anthropology, 61(S22).

Sean Cubitt, Robert Hassan, and Ingrid Volkmer (2011). Does Cloud Computing Have a Silver Lining?. Media, Culture & Society.

Textbooks

Ruha Benjamin (2019). Race after Technology: Abolitionist Tools for the New Jim Code.

Geoffrey C Bowker and Susan Leigh Star (2000). Sorting Things out: Classification and Its Consequences.

Catherine D’Ignazio and Lauren F. Klein (2020). Data Feminism.

Bernard Cohn (1996). Colonialism and Its Forms of Knowledge : The British in India.

Mar Hicks (2017). Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing.

Anne Alexander et al (2021). Ghosts, Robots, Automatic Writing: An AI Level Study Guide.

Hannah Turner (2020). Cataloguing Culture: Legacies of Colonialism in Museum Documentation.

Mary Gray and Siddharth Suri (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Project proposal

Assessment Type: Formative
Feedback: Feedback is ongoing and forms part of the teaching as a whole. The students will receive written and verbal feedback on all of their assignments.
Final Assessment: No
Group Work: Yes

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Portfolio	50%
Public outcome	50%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Public outcome	50%
Portfolio	50%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Portfolio	50%
Public outcome	50%

Repeat Information

Repeat type: Internal & External