Data Literacy

Special track chairs: Julia Stoyanovich (New York University), C. Leigh Anderson (University of Washington), Sarah Giest (Leiden University), Diego Kuonen (University of Geneva)

All regions

The standard tracks of Data for Policy 2022 cover a multitude of topics in data-driven innovation, policy-making and decision-making, data and algorithmic governance, and ethics, equity and trust. All tracks also include a focus on the interactions between citizens and institutions in the context of policy and governance. Implicit in these topics is the importance of data literacy on the part of a range of stakeholders: policy makers and decision makers, data ethics offices and data stewards, individuals affected by data-driven decisions, and the public at large. This special track aims to frame an explicit and deliberate conversation around how to be a critical consumer of data and other data literacy issues, outlining data literacy needs of different stakeholders, and discussing methods for improving data literacy in specific domains. 

In this context we interpret data literacy as the ability to read, understand, critically collect, manage, evaluate, and apply data, noting that this interpretation may be different for different stakeholders (Misra, Juetting and Kuonen 2021). Similar to literacy more generally, data literacy points towards the competencies involved in producing and using data and has effects on how data is collected and utilized in different (policy) contexts. This is especially relevant during a time when governments are increasingly looking at algorithmic applications where data competencies are a key aspect to identifying, for example, data gaps. 

The scholarship on data literacy has broadened in recent years and addresses different dimensions of the concept as well as different perspectives. For example, some studies understand data literacy as a term for teaching citizens to read, use and work with data – often in an empowering way (e.g., D’Ignazio 2017), as well as fostering people’s “data mindset” (D’Ignazio and Bhargava, 2018; Sander 2020). Others include a critical reflection of (big) data systems, which includes the terms of “AI literacy” (Long and Magerko, 2020), “algorithmic literacy” (Grzymek and Puntschuh 2019), and “data infrastructure literacy” (Gray et al. 2018). The Special Track welcomes submissions that build on and extend such work in the policy context. 

This track will cover the following topics: 

  • Data literacy needs of different stakeholders 
  • Case studies on improving data literacy in specific domains 
  • Educational materials and methodologies 
  • Data literacy and public engagement 
  • Data literacy across different sources and types of data