Aims and focus

Home > Current research – Living With Data > Aims and focus

abstract image

On Living With Data we aim to: 

  1. advance understanding of people’s knowledge, experiences and perceptions of data practices, by asking What do different people know and feel about specific data-related practices in different domains of everyday life?
  2. advance understanding of people’s perspectives on how data-related practices could be improved, by asking What do fair data practices look like, from non-experts’ perspectives?
  3. share findings with relevant stakeholders.


We will take selected public sector data practices which relate to healthcare, accessing public services and media use as a starting point for our empirical research. We have selected cases from the public sector because its data practices increasingly shape everyday life experiences, and yet they have received less attention than high profile commercial data practices. 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 pressing need to understand what public sector organisations do with data about the public and related consequences.

A small number of researchers have recently noted that disadvantaged populations are more likely to experience datafication negatively than others (eg Eubanks 2017, Noble 2018). It is therefore important to take account of how social inequalities may lead to different experiences and perceptions of data and related practices. Hence we ask what different people know and feel about data practices, to enable us to focus on the views and experiences of disadvantaged populations. We will oversample disadvantaged groups for inclusion in our research. Published research (eg Eubanks 2017) highlights how class and race contribute to differential data experiences; pilot research by team member Kennedy and others has identified the importance of age and ability. Thus we will prioritise these four factors, and gender and sexuality, two other known indicators of disadvantage. 


Eubanks, V. (2017) Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor. St Martins Press: New York.

Noble, S. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press: New York.