What is ‘data justice’?

There are a number of different ways in which we might talk about making the use of digital data more equitable, empowering and ‘fair’. While these approaches have similar-sounding names (and often share similar aims) there some important distinctions to be made. For instance, while issues of ‘Data Rights’ and ‘Data Ethics’ continue to attract attention, interest is also growing in the specific idea of ‘Data Justice’.

This emphasis on ‘Justice’ is a deliberate attempt to move beyond seeing data as an individual problem and responsibility. Instead, data is framed as a collective concern that is connected with broader patterns of injustice. As Hintz and colleagues (2018, p.140) put it:

“the aim would be to situate datafication in the context of the interests driving such processes, and the social and economic organisation that enables them. From this, it would be possible to engage with societal implications of datafication in relation to other social justice concerns”.

These themes are expanded in Linnet Taylor’s (2017) framework for data justice – again stressing the connections between data rights (the power to better engage with data) and data freedoms (the power to choose how to engage with data). As Taylor puts it, this encompasses “the need to opt out of data collection or processing, the need to preserve one’s autonomy with regard to data-producing technologies and the need to be protected from and to challenge data-driven discrimination”. These rights and freedoms can be seen in terms of ‘three pillars’ of data justice:


Making these connections between data and broader structural issues of societal inequality, discrimination and oppression raises a number of significant contentions:

  • First, issues surrounding digital data are not seen as purely technical and/or organisational problems with likely technical and/or organisational solutions. Instead, digital data is understood as entwined with long-standing societal problems such as discrimination, social immobility, and the social reproduction of inequality. These issues are social, cultural, political and economic in their origins and in terms of any likely responses to address them.
  • Second, issues surrounding digital data are not seen as an individual concern. Any problems with digital data are not due to an individual’s personal choices or individual biases. Neither are individuals ultimately responsible for dealing with these problems. Instead, the idea of data justice reminds us that “the issues are structural” (Noble 2019). As such, these are issues that require collective responses and a wide range of stakeholders being involved “in articulating both challenges and responses to datafication” (Hintz et al. 2019, p.136)
  • Third, framing datafication in terms of (in)justice prompts us to consider collective political responses and direct actions. For example, viewing data issues in terms of ‘rights’ can often result in responses relating to improving individuals’ consumer rights (for example, clearer and fairer terms of service), recommendations for direct consumer action (for example, individuals deleting their Facebook accounts), or pushes for stronger regulatory and legal protection in law. In contrast, the notion of data justice raises the prospect of direct collective actions. Data justice does not raise issues that are likely to be solved by the market or by better regulation. Instead, it highlights issues that require protest, confrontation, disobedience and disruption.

All told, the data justice approach is infused with an intention to make data a political issue. In an era of ‘digital resignation’ and ‘surveillance realism’, we cannot expect people to automatically challenge data-driven disadvantages and inequalities that critical commentators might assume are obviously problematic. Instead, politicisation is a necessary first step in generating the possibility of change. In this sense, the data justice approach is imbued with a spirit of collective activism and provocation …

“a wider collective moment can shake the foundations of the datafication paradigm, to reveal what else might be possible” (Hintz et al. 2019, p.142).