DATA, AI AND SOCIAL JUSTICE - 2027/8

Module Overview

This module examines how data-driven and AI-mediated technologies shape social life, power relations and justice in contemporary societies. Students explore theories and lived experiences of datafication, automated decision-making, algorithmic profiling, platform governance, AI ethics and accountability. Through case studies spanning surveillance, everyday platform use, biometric and wearable technologies, children¿s technologies, disinformation and generative AI, students learn to critically analyse how data and AI systems create benefits, risks and inequalities.
The module blends conceptual teaching with grounded, observational and applied work. Students develop the ability to interrogate real-world examples of data practices, identify harms and opportunities, and articulate socially responsible interventions. They build the analytical, critical and communication capabilities required to engage thoughtfully with emerging technologies as researchers, citizens, and future professionals.

Module provider

Sociology

Module Leader

DAS Ranjana (Sociology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

NA

Module content

Theories and concepts; Datafication and life in platform societies;  Social consequences of algorithms and automation;  AI systems and their societal outcomes Case studies; AI-enabled disinformation and deepfakes;  Datafied selves and relationships in an AI age;  Children, data and AI;  Querying facial recognition systems; Critical issues; In/justices in platform and AI-mediated societies; Content moderation in the age of AI;  Hope, possibility and doing good with data and AI; Assignment preparation workshop Additional content will address newly emerging technologies and contemporary debates as they arise.

Assessment pattern

Alternative Assessment

Students who are unable to undertake the in-person Data Walkshop demonstration and/or the in-person scenario exercise will complete an alternative assessment that evaluates the same learning outcomes at the same academic level. The alternative assessment will consist of: Alternative Assessment (100%) ¿ Applied Data & AI Case Analysis Portfolio Students will submit an individual portfolio comprising: Virtual Data Walkshop Analysis (60%) An individually produced analytical report based on a guided virtual ¿data walk¿ using publicly accessible digital environments (e.g. platform interfaces, AI-enabled services, policy documents or media case materials). Students will critically analyse instances of datafication and AI, identifying social justice implications, risks and opportunities, supported by relevant theory and literature. Scenario-Based Critical Analysis (40%) A structured written response to a previously unseen scenario involving a societal issue shaped by data and AI technologies. Students will apply concepts and case studies from across the module to analyse the problem and propose context-sensitive responses.

Assessment Strategy

Assessment pattern

Assessment 1 (60%) ¿ Data Walkshop demonstration
Students undertake an in-person ¿Data Walkshop¿ on campus and Guildford town, documenting instances of datafication and AI in everyday spaces around Guildford (photographs, screenshots, on-site observations). Working in small groups, they produce a short in-class demonstration (using either printed out, sketched out or slides-format images and notes) - using only their own images, offering a critical account of the sociotechnical issues revealed. This assesses observational skill, critical awareness and the ability to articulate social implications of data and AI systems - actively applying knowledge and abilities in real-world contexts.

Assessment 2 (40%) ¿ In-person scenario exercise
In a 2 hour, in-class controlled assessment, students respond to a previously unseen scenario involving a societal problem caused, exacerbated or mitigated by data and AI technologies. They apply theories, concepts and case study knowledge from across the module to offer a structured critique and outline possible responses. The scenario format is rehearsed throughout the semester in seminars. 

Assessment strategy

The strategy enables students to demonstrate:

1. Real-world applications of theories and concepts related to datafication and AI
2. Capacity to apply theoretical and conceptual tools to socio-technical problems.
3. Understanding of social justice implications of data and AI.
4. Ability to communicate analysis clearly in spoken and written forms.

The Scenario exercise assesses conceptual integration, critical reasoning and the ability to work under timed, in-person conditions.

The Data walkshop demonstration assesses observational, analytic and oral communication skills grounded in authentic, real-world materials.

Formative assessment and feedback

Students receive formative practice through weekly scenario exercises, in-class discussion, and preparation workshops. An interim scenario rehearsal early in the semester and a walkshop workshop later in the semester will provide personalised feedback to support performance in the summative assessment.

Module aims

• Equip students to research, analyse and critically evaluate the societal implications of datafication and artificial intelligence in contemporary societies.
• Develop students¿ conceptual and theoretical understanding of how data- and AI-driven technologies shape power relations, inequality and social justice across diverse global contexts.
• Enable students to investigate real-world cases of data and AI in everyday environments, applying observational and analytical methods to sociotechnical issues.
• Support students in developing and justifying responsible, context-sensitive responses and recommendations for addressing risks and opportunities associated with data and AI systems.
• Strengthen students¿ abilities to communicate evidence-based analysis, critique and proposals effectively to academic and non-academic audiences using appropriate oral and written formats.

Learning outcomes

Methods of Teaching / Learning

Teaching and learning methods 

Teaching integrates lectures, workshops, seminars and practical activities, including: Scenario analysis, Data Walkshop, short presentations, Case study discussions, Concept explainers, Video and policy analyses, Role-play and applied exercises.

 These methods support students in working with evolving technologies and understanding their societal and ethical implications.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: SOC3091

Other information

Attributes developed Digital capabilities: Students analyse real-world data practices, interact with AI-related tools and contexts, and develop critical digital literacy. Global & cultural capabilities: Case studies address data justice, inequality and AI governance across global contexts. Employability: Students produce analyses, recommendations and presentations relevant to sectors engaging with digital and AI systems. Resourcefulness & resilience: Fieldwork, timed tasks and applied critique develop adaptability, problem-solving and confidence.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.