Impacts on inequality

Work on open data and transparency can sometimes be blind to the political and often contested nature of data, and to programmes’ potential effects on local dynamics.

In a project mapping new technologies emerging in transparency and accountability, Avila et al. (2010) found that initiatives “need to be designed intelligently and with an eye towards local context [and] must be careful to avoid exacerbating societal inequalities by disproportionately empowering elites”. One of the inequalities that open data could widen is the digital divide (Verhulst, 2016). Recent research by MySociety (Rumbull, 2015), a civil technology NGO, suggests the primary beneficiaries of civic technologies built on government data or services are privileged populations.

Gurstein (2011), quoted in McGee and Edwards (2016) reflects from within the open data movement on whether open data is about enabling effective data use for everyone or “empowering the empowered”. He notes the absence of specific efforts to ensure the widest possible availability of the prerequisites for “effective use”, and fears the outcome of open data may be the opposite of that anticipated by its strongest proponents.

Critiques of a perceived blindness to social inequality come from outside and inside the community of actors working on open data. In his analysis of the field, Kitchin (2013) argues that an assumption that open data is “neutral and objective” is implicit in most discussions on the topic. Writing from an information justice perspective, Johnson (2014) contends that there are “problems of justice” resulting from opening data to full public accessibility, stemming from a “failure of the open data movement to understand the constructed nature of data”. He sees value structures as inherent in datasets, which shape analysis and interpretation and work to propagate injustices and reinforce dominant interests. Chow-White (2008) highlights that, despite appearing neutral, when information is networked, such as when databases are compiled and linked, it is not simply reflective of the social world but rather plays “a constitutive role”.

Difficulties are especially evident in relation to gender. Data and metadata may perpetuate gender stereotypes, by, for example, not making provision for non-normative gender identities, capturing only paid occupations or registering males as default property owners (see Buvinic & Levine, 2015; Tichom, 2015).