In collaboration with organisations involved in its development and promotion, the aim of this project is to critically document and analyse the making of a new collective ethnic identifier in the United Kingdom: “East and Southeast Asian” (ESEA).

The term ESEA has emerged relatively recently in the UK, coming to prominence since 2018. This term is typically used as a bottom-up collective ethnic identifier for communities who originate from, or have ties to, East and Southeast Asia. Increasingly, it also functions as means of categorising communities to secure political recognition and representation.

In the past few years, a number of different activist groups and civil society campaigns have begun to use ESEA for at least three key reasons:

1. In solidaristic response to the intensification of anti-Asian racism and violence spurred by the COVID-19 pandemic, and racism against BIPOC highlighted by BLM;
2. To campaign for the institutional inclusion of ESEA communities (In the UK, the word “Asian” typically refers to people of South Asian origin); and
3. As a form of political community-building.

The emergence of this term provides us with a rare opportunity to study a collective ethnic identifier in the making. Using digital methods and approaches derived from critical code race studies, we hope to produce research outcomes that our collaborating organisations can use in their advocacy work, while also producing a better understanding of how and why such collective community identifiers emerge and how they come to be institutionalised as categories.

Despite substantial scholarship on particular forms of personalisation, there are few attempts to establish the broad cultural significance of new practices of personalisation. It is unclear how far these practices are replacing a one-size-fits-all universalism in markets, welfare services, and consumer culture.

We assume that personalisation is a key process in contemporary life, affecting emerging technologies, processes, and imagined futures. This project investigated personalising practices as they are formed in different domains, while paying close attention to the points of interaction and contradiction between them.

Our work established histories and techniques of personalisation as they affected a variety of individuals working in and beyond the industries for which ‘personalisation’ is an important process – e.g. medicine and data science, public policy, advertising and more. Based on in-depth interviews and focus groups, we contrasted the views of industry practitioners and consumers with historical and theoretical accounts of personalisation.

For more information see Goldsmiths data and governance.

Paying particular attention to the ways in which individuals are tracked, categorised and contextualised, this project investigates how data are collected, managed, interrogated and utilised in science research and translational medicine more broadly, as well as how these processes have been impacted by Covid-19. Over the past 20 years, data used in healthcare research has shifted in terms of what types of data are harnessed for analysis, what processes are captured, the granularity of these data captures, and through the capturing of temporality, in part by means of data linkage. For this interview study we recruited researchers for semi-structured interviews where they discussed all facets of data processes as they relate to personalising processes including: informatics, data processing, analysis, de-identification, methods and application in research and clinical settings.

Early in 2020, a key part of the COVID-19 response in the UK was to identify people at risk of poor outcomes in order to “shield” them from infection. 6.8% of the English population were initially identified as at high risk of having severe outcomes from COVID-19 and were added to the Shielded Patients List. We have researched the processes of creating and then applying this new category of person as a case study on how data are used in personalisation. This Shielded Patients List was created initially using a clinical algorithm applied to healthcare data over a period of weeks, then added to by clinicians, and later expanded to include more people at high risk of severe outcomes by utilising a personalised risk score algorithm for predicting COVID outcomes derived from healthcare data during the first wave of the COVID-19 pandemic. The technologies that underpin the creation of this list- data sharing, data interoperability, the application of algorithms, user generated data, data systems, machine learning – all existed before the Covid-19 pandemic, but the creation of the Shielded Patient List represents a novel shift in data practices and how they are imagined within the medical and scientific fields, particularly with regards to healthcare data and wide scale personalisation and population health management.  As a part of this work, we collaborated with Patient Experience Research Centre (PERC) to develop and administer a questionnaire to those who were on the Shielded Patients List to understand how they were impacted by the creation of this category. We also examined the Shielded Patients List methodology to explore key features of personalisation such as the need for participation (in data sharing), precision (in defining a category), prediction (of an outcome given that category) which then leads to some action.

The Rio Trial tests whether a new type of therapy can keep HIV under control without daily antiretroviral treatment (ART) tablets. The novel therapy uses a combination of two experimental antibodies (called broadly neutralising monoclonal antibodies, or bNAbs) which have been designed by scientists at the Rockefeller University. We are interviewing Trial participants, clinical and research staff, as well as those people unwilling or ineligible to participate. We will try to understand the expectations and experiences of participation in the RIO Trial. In the historical context of HIV medicine and research, we will explore developments in personalised medicine and care, with associated ideas about the person and their health.

Our research is a sub-study of the RIO Trial. We work in collaboration with Chief Investigator Sarah Fidler (Imperial College NHS Trust), Co-Investigators Michel Nussenzweig (The Rockefeller University) and John Frater (University of Oxford), Trial staff and community representatives.

Ethics for the RIO Trial and our sub-study was approved by the NHS Health Research Authority, London – Westminster Research Ethics Committee, 19/LO/1669.

Profiling and recommendation are a part of the online services we use every day. These services suggest things we might like to buy or people we might want to connect with. They recommend songs, books, movies, and television shows that we might want to listen to, read, or watch. Sometimes they get these recommendations right. Sometimes, though, they get these recommendations very wrong, making strange or even comical inferences about who we are from what we do online.

Algorithmic Identities aims to contribute to the social studies of data and algorithms by formulating innovative research methods for studying algorithmic recommendations. In this emerging field, considerable attention has been paid to the social and technical aspects of algorithms themselves. Far less attention has been paid to how users perceive and understand algorithms. In response, we devised this project to understand how people from Chile and the United Kingdom feel about the extraction of digital data and the algorithmic inferences that are made about them. What are recommendation systems? How do they work? How do they shape our online experiences? How do people feel about them? What does it feel like when they get us right? Or when they get us wrong?

To explore recommendation systems, we created a prototype smartphone app called Big Sister. We combine techniques of digital trace collection, qualitative methods and visualisation to open up questions about participation, use and users, habit, and the binary of subjects and objects. We have devised this project as a participatory experiment in “prototyping.” By asking participants to use this app and to reflect on their experiences of recommendation systems online, we hope to make profiling and recommendation systems more transparent.

The Algorithmic Identities project is a collaboration between members of the People Like You team and Martín Tironi and Matías Valderrama Barragán from the Pontificia Universidad Católica de Chile. Alongside People Like You’s funding from the Wellcome Trust, it is being conducted under the auspices of the Fondecyt project N°1180062: “Datafication of urban environments and individuals: an analysis of the designs, practices and discourses of the production and management of digital data in Chile” and has also been supported by the “Interdisciplinary Research 2018 Funding” from the Vice-Rectory for Research (VRI) of the Pontificia Universidad Católica de Chile.

Personalised medicine aims to replace a one-size-fits-all approach with the ‘right treatment for the right person at the right time’. Achieving this requires broad participation of patients willing to share data and biological samples which are then analysed and utilised in the personalisation process.

Previous work suggests that certain categories of participant are systematically under-represented in biobanks, resulting in biased data sets. It is possible therefore that “people like you” are not well represented in these biobanks; we are interested in how this affects the practice of personalised medicine, and whether different approaches to consent, sharing and use of data may help to reduce inequity.

Our review of the published literature (in collaboration with Dr Bethan Davies and Professor Paul Elliott at Imperial college) indicates that there is no consensus on either the definition, purpose or need for representativeness in biobanks. Some argue that it is unnecessary or even counter-productive to include a representative sample of the population, but there is little discussion of how this may exclude some marginalised groups from the benefits of translational research.

Our ‘Perspectives on Consent’ empirical project, in which we were documenting the establishment of a local biobank, the Imperial Health Knowledge Bank, was interrupted by the COVID-19 pandemic when much clinical research was suspended.  The infrastructure was largely re-purposed for research on COVID-19 and we shifted our resources towards understanding participation and bias in large population studies in the pandemic.

Although some processes were interrupted, others were accelerated during the pandemic, particularly the linkage of different datasets and making those available in anonymised form for research. We are continuing to track these developments which are building up the data infrastructure and bioresources necessary for personalising practices in health. We will summarise findings in publications towards the end of the project, continuing to reflect on the impact of missing people and missing observations in big data.

The “perspectives on consent” study has approval from the NHS Health Research Authority, ID: 245816

Personalisation is a pervasive part of digital culture. Its applications include tailoring the content we see online to suit our presumed interests; targeting advertising; predicting the trustworthiness of loan applicants; or even shaping the delivery of government programmes. It’s clear that personalisation has become a regular feature of online services and our online interactions. In fact, we often take it for granted. But its broader impacts on how society is organised and governed and how it informs our participation in culture and politics are still emerging.

Personalisation in digital culture is changing the way we are grouped, sorted, and categorised. We explore these emerging forms of grouping through three case studies. First, we use an analysis of politicised hashtags – such as #JesuisCharlie and #MeToo – to examine how personalised modes of address create new group forms. These hashtags address groups: ‘People Like You.’ But the ‘You’ they address is always both singular and plural. By studying them, we show how they reconfigure issues of inclusion and exclusion, intensify oppositional dynamics, and lead to the amplification of differences between rival groups. Second, we investigate how personalisation generates new, sometimes reductive, modes of categorising people. Somewhat counter-intuitively, digital culture’s services and platforms can only be made personal by comparing ‘You’ to ‘People Like You’ – in other words, by sorting people into categories with other people whom they are ‘like.’ The promise of these techniques is that they can supersede older, blunter demographic categories, like race. By examining novel categories generated by machine learning – like Facebook’s ‘ethnic affinities’ – we ask how personalisation actually changes how we’re categorised and racialised today.  Third, we are also exploring how we are invited to participate in organisations and institutions such as ‘MyUniversity,’ which we consider to be a personalised generic. We consider whether and how such generics operate as brands or assets, a source of revenue and reputation.

Building on the recognition that personalisation requires participation, we also describe and investigate the various ‘genres’ this now takes. We have designed a public competition with design agency Rectangle to stage participation and examine its dynamics. In collaboration with colleagues from the Pontificia Universidad Católica de Chile in Santiago, Chile, we have also developed a prototype recommendation system, in the form of a smartphone app, that we use as part of a participatory study examining how people understand, engage in, and manipulate personalised recommendations (see Algorithmic Identities). Finally, we have also developed a collaboration with the artist Felicity Allen, who is developing a series of ‘dialogical portraits’ that use analogue techniques to examine portraiture and representations of the self today.

For more information see Warwick CIM and data management.

‘Personalised’ approaches are transforming the treatment of breast cancer and we explored developments in a research-intensive London hospital trust. We followed up a previous study of the breast cancer pathway (conducted 2013-14 at Imperial College London Healthcare NHS Trust) to learn about changes in the last 5 years. We explored staff and patient perspectives on two translational research studies. And finally, we gained an understanding of broader perspectives on personalisation by evaluating participation and public involvement in Science Café meetings and through a Poetry Residency with Di Sherlock at Maggie’s West London, which is associated with the Trust. We described developments in personalised medicine and associated ideas about the person and their health, including the role of participation and what people think about the collection, use and sharing of data. In 2020-21 we extended this study in the breast cancer service. This allowed us to understand how the COVID-19 pandemic affected the organisation and delivery of personalised medicine and research.

Ethics was approved by NHS Health Research Authority, North West—Greater Manchester West Research Ethics Committee, 18/NW/0550.

Visit Publications and Art Residencies to learn more.

For a short summary of our research, download our booklet Personalised Breast Cancer Medicine and Healthcare (2022).