Aims and focus
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Aims
On Living With Data we aim to:
- advance understanding of people’s knowledge, experiences and perceptions of data practices,
- advance understanding of people’s perspectives on how data-related practices could be improved,
- share findings with relevant stakeholders.
We are meeting these aims by asking:
- What do different people know and feel about specific data-related practices in different domains of everyday life?
- What do fair data practices look like, from non-experts’ perspectives?
Focus on public sector data practices
We take public sector data practices which relate to health, welfare and media use as a starting point for our research. We have selected cases from the public sector because public sector data practices increasingly shape everyday life experiences, and yet they had received less research attention than high profile commercial data practices when we started our research. The Digital Economy Act (of April 2017), which enables data sharing across government departments, is one indication of the public sector’s growing use of data and of the need to understand what public sector organisations do with data about the public and the consequences of these practices.
Focus on social inequalities
A small number of researchers have noted that already-disadvantaged populations are more likely to experience negative consequences from datafication. These include Virginia Eubanks (2017) and Safia Noble (2018). It’s important to understand how social inequalities may lead to different perceptions of data and related practices, as well as different experiences of data practices. This is why we ask what different people know and feel about data practices, to enable us to focus on the views and experiences of disadvantaged populations. For this reason, we have oversampled people from disadvantaged communities in our research. Virginia Eubanks (2017) highlights how class and race contribute to differential data experiences. Research by Living With Data director Helen Kennedy and others (2020) has identified that age and dis/ability influence perceptions of data practices. In our research, we have prioritised these four factors, and also gender and sexuality, as two other known indicators of disadvantage.
References:
Eubanks, V. (2017) Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor. St Martins Press: New York.
Kennedy, H., Steedman, R. & Jones, R. (2020) Approaching public perceptions of datafication through the lens of inequality: a case study in public service media. Information, Communication and Society.
Noble, S. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press: New York.