Algorithms for Marginalised People

Researching media and algorithmic bias

The AMP research unit has been set up to challenge normative assumptions in online media and the algorithms that control their distribution and presentation. Its cross-disciplinary team of media and machine learning specialist work with marginalised audiences to reflect their perspectives and create tools to combat bias.

AMP is a Manchester, UK, based research group comprising researchers at both Manchester Metropolitan University and University of Manchester. Through a collaboration between both machine learning experts and media theorists and practitioners it aims to develop new techniques in data collection, machine learning processes and application development that considers the both raced and gendered perspectives of technology. Through a focus on marginalised audiences it seeks to outline the distinct needs of people typically discounted by algorithms as statistical outliers. 

AMP projects are variously supported by the AHRC, NWCDTP, NNMHR, Building Connections/Big Lottery Fund, Co-Op Foundation and Wellcome Trust




Likely Stories: AHRC funded studentship

Can we predict the persuasiveness of online media based on composite elements such as such as musical arrangements, storylines and imagery? The Likely Stories project explores the use of AI in detecting media bias and predicting its effect on marginalised young people in the UK and Pakistan, in partnership with the National Centre for AI in Islamabad. The project responds to a lack of research into using machine learning to understand the aesthetics of persuasion in audio-visual narratives in the short form videos common on social media, especially in relation to marginalised audiences. How can arts and humanities theory inform and enhance machine learning approaches to problems of media bias and persuasive intent?

Likely Stories PhD studentship is funded by the North West Consortium Doctoral Training Partnership and the Arts and Humanities Research Council.


Unhealthy bias: Wellcome trust seed fund project

Research into public health campaign impact (Gregory et al., 2010) suggests that failures in persuading UK South Asian heritage families contribute to health problems, such as heart disease. Algorithmic responses to problems of media bias have the potential to challenge biases. However, for these data-led methods to be effective requires proper examination of the ‘normative technocultural beliefs’ that obscure ‘raced and gendered’ influences on online behaviour (Brock, 2015:1087).This project will use media theory and its methods to inform machine learning (ML) approaches to NHS public health online video efficacy for South Asian audiences.

Unhealthy Bias has been awarded seed funding by the The Northern Network for Medical Humanities Research and the Wellcome Trust. 


Mood/Music: helping young carers cope with loneliness

Young carers are a hard-to-find group who experience isolation and loneliness because of the time and energy they put into caring for a loved one. In partnership with the Gaddum Centre charity, this arts-based project seeks to work with a small group of young carers to co-create music tools that express their experiences as a group and as individuals. Using cutting edge AI emotion sensing technologies it will use music to create a map of the emotions of young carers over a day a week and a month and work with carers to find out how music can intervene.

Mood/Music is funded by an award from The Building Connections Fund a partnership between Government, Big Lottery Fund and the Co-op Foundation



The team combines specialisms in both artificial intelligence and media to provide interdisciplinary research approaches.


Dr Toby Heys

Dr Toby Heys is a reader in Digital Media and head of research for the School of Digital Arts (SODA) at Manchester Metropolitan University. He has a cross-disciplinary research and practice profile but his dominant focus is how music and sound are utilised by governments and industry to influence, manipulate and ultimately torture the individual and collective social body. 


DR David Jackson

Dr David Jackson is a post-doctoral research fellow at Manchester Metropolitan University. His research considers the creative application of AI technology in narrative contexts and how these new forms affect their audiences. 


Dr TingTing Mu / associate member

Dr Tingting Mu is Lecturer in Text Mining at University of Manchester. Her research is focused on developing advanced mathematical modelling and large-scale optimisation techniques to simulate human intelligence and analyse real-world complex data. 


Dr Raheel Nawaz

Dr Raheel Nawaz is a Reader in Text and Data Mining at the Manchester Metropolitan University. He heads the Digital Transformations Research Cluster, and is the Founding Head of the Text and Data Mining Lab. He holds adjunct and honorary positions with several research organisations, both in the UK and in Pakistan.


Dr Keeley A Crockett

Dr Keeley Crockett is Reader in Computational Intelligence in the School of Computing, Maths and Digital Technology at Manchester Metropolitan University and is a founding member of the IEEE Taskforce Ethical and Social Implications of Computational Intelligence. Her research focuses on machine learning, specifically psychological profiling and fuzzy language dialogue systems.


Dave Mee / associate member

David Mee is a PhD research student with significant experience in using digital culture to reach community audiences. He set up and co-ran Manchester digital maker spaces MadLabs for many years. His research focuses on how extremist groups use networks to reach and grow audiences.


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If you would like to get in touch with us at AMP, to discuss potential project work or media opportunities for example, please use the form below and we will respond to your query. 

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