MA Digital Media, Culture and Society / Course details

Year of entry: 2025

Course unit details:
Artificial Intelligence, Algorithms, and Society

Course unit fact file
Unit code DIGI61112
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Available as a free choice unit? No

Overview

This unit will explore and examine the growing effect of artificial intelligence (AI) and algorithms on, and in, society. AI and algorithms are increasingly part of everyday life. From Midjourney to ChatGPT, AI is transforming the way people work, play, experience the world, and act within it. But what do AI and now dominant algorithmic methods such as machine learning (ML) involve, and what are the social, cultural, and political implications of their development and use? In this course unit we will explore what AI and algorithms do, and how artificial, algorithmic, and autonomous systems are being designed, developed, tested, funded, and built – with social, political, and cultural consequences.

Aims

  • To introduce students to the role and effects of AI and algorithms in contemporary societies
  • To offer an overview of current debates around AI and algorithms in society
  • To provide a critical understanding of the possibilities, problems, and ethics of AI and algorithms

Syllabus

The course unit is divided into two (parallel) sections, corresponding to the lecture component and the seminar component. In Section I: Understanding AI students will consider what AI is (and isn’t), the history of automation, what machine learning involves, how big tech platforms dominate the world of AI, and what the ensuing environmental impacts of an AI-driven world are. In consecutive weeks, students will explore different aspects of AI in their situated, social contexts, from the practices of ‘ground-truthing’ to AI industrial policy. 

In Section II: Exploring AI students will have the opportunity to experiment with key aspects of the AI ‘pipeline’ through practical activities and workshops. Following a wide range of experimental, creative approaches, students will learn how data is prepared for AI purposes, how machine learning models are designed and tested, and how AI services and competitions are delivered and organized. In the second half of the course unit, students will complete a project designed to examine the growing social, cultural, and political pressures AI is exerting on the world.

Teaching and learning methods

The unit consists of 1-hour lectures, 2-hour seminars, and 1-hour drop-in sessions. The lecture component will ordinarily consist of introductions to each AI topic, as well as key epistemological, ontological, political, cultural, and ethical debates around them. The seminar component will consist of (guided) practical workshops in which students will have the opportunity to creatively use, explore, and experiment with different AI tools, platforms, software, services, and platforms. The seminar component will also be used for students to develop their group projects, offering the opportunity for collaborative experimentation as well as the space for informal feedback and advice. All supporting material will be provided via a corresponding Blackboard course unit page. Drop-in sessions will allow students to hone assessment work if desired.

Knowledge and understanding

  • Critically evaluate the role of AI and algorithms in contemporary societies from a cultural, technical, and political perspective
  • Examine how AI and algorithms are employed within contemporary societies, and how their use has different practical and ethical consequences
  • Interpret and analyse how AI and algorithms are developed, designed, and operated
  • Develop oral and written forms of interpretation and argumentation through a critical engagement with textual material, hands-on examination of technical objects, seminar discussions, and essay writing  

Intellectual skills

  • Apply analytical skills to examine and analyse the effects of AI and algorithms in society
  • Demonstrate knowledge and understanding of critical debates on AI and algorithms within media studies and related fields
  • Articulate and explain key concepts concerning the implications of AI and algorithms for contemporary work, leisure, politics, culture, and the economy
  • Demonstrate an awareness of the methodological approaches to understanding AI and algorithms, from different technical, cultural, and political perspectives

Practical skills

  • Engage in oral and written debates on a broad range of AI and algorithmic topics
  • Build argumentative frameworks for the analysis of AI and algorithms from cultural perspectives
  • Use digital and non-digital research materials and resources
  • Follow academic referencing standards and norms in academic writing assignments
  • Perform independent research involving the careful selection of appropriate material, topic, and case studies

Transferable skills and personal qualities

  • Present information, ideas, arguments, and methodologies in respect to relevant parties
  • Active and constructive participation in group activities
  • Understanding and assessment of different perspectives
  • Demonstrate analytical abilities
  • Demonstrate critical evaluative skills in relation to AI, algorithms, and digital media

Employability skills

Analytical skills
Deploy critical evaluative skills to situations or settings where AI and algorithms might be used
Innovation/creativity
Respond and adapt to criticism levelled at applications of AI and algorithms
Problem solving
Understand how AI and algorithmic services might discriminate against and alienate certain users
Other
Develop an understanding of how AI and algorithms can be used in an applied context

Assessment methods

Method Weight
Written assignment (inc essay) 100%

Feedback methods

Feedback method

Formative and/or Summative

Written (Blackboard, email)

Formative

Verbal (office hours, in-class)

Formative and summative

Turnitin

Summative

Recommended reading

Amaro, R. (2022) The Black Technical Object: On Machine Learning and the Aspiration of Black Being. Berlin: Sternberg Press.

Amoore, L. (2020) Cloud Ethics: Algorithms and the Attributes of Ourselves and Others. Durham, NC: Duke University Press.

Andrejevic, M. (2019) Automated Media. London: Routledge.

Crawford, K. and Joler, V. (2018) Anatomy of an AI system. Anatomy of AI https://anatomyof.ai/

Golumbia, D. (2009) The Cultural Logic of Computation. Cambridge, MA: Harvard University Press.

Hayles, K. (2017) Unthought: The Power of the Cognitive Nonconscious. Chicago, IL: The University of Chicago Press.

Jaton, F. (2021a) The Constitution of Algorithms: Ground-Truthing, Programming, Formulating. Cambridge, MA: MIT Press.

Mackenzie, A. (2017) Machine Learners: Archaeology of a Data Practice. Cambridge, MA: MIT Press.

Roberge, J. and Castelle, M. (2020) (eds.) The Cultural Life of Machine Learning: An Incursion into Critical AI Studies. Cham: Palgrave Macmillan

Study hours

Scheduled activity hours
Lectures 6
Seminars 12
Tutorials 4
Independent study hours
Independent study 278

Teaching staff

Staff member Role
Sam Hind Unit coordinator

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