Abstract

Data and AI literacy is often misunderstood as a set of technical skills, limited to data management and analysis and to the development and application of algorithms. However, data and AI literacy as a future skill of the 21st century serves to promote autonomy in a modern world shaped by data and its application as well as new technologies like AI and is therefore important for all people - not only for specialists. Data and AI literacy therefore encompasses a set of cognitive, meta-cognitive, affective, and socio-emotional competencies, which are grounded in universal moral values and enable individuals to face the challenges of digital life and adapt to its demands.
There is also a need for a standardized framework to capture at least a minimum set of foundational and cross-cutting data literacy competencies relevant for an individual, organization, or system. This will help to identify clear data literacy needs, support the effective targeting of policies and programs to enable data literacy, and provide a benchmark to assess the impact of such efforts. Developing a suitable standard and measures that allow for a fit-for-purpose tracking of progress on data literacy will bolster the case for investing in data literacy beyond ad-hoc programming.
According to the definition in the global standard IEEE 3517.1-2020, Data and AI Literacy is “the ability to generate, process, analyze, present meaningful information from data and develop, use, and apply artificial intelligence (AI) and related algorithmic tools and strategies in order to guide informed, optimized, and contextually relevant decision-making processes”. This standard, often referred to as “Digital Intelligence (DQ) Standard”, organizes competencies in three competence dimensions: specific knowledge (dimension "knowledge"), the skills and competencies to apply this knowledge (dimension "skills"), and the willingness to do so, i.e., the corresponding ethics, values, and attitudes (dimension "values"). A similar perspective is taken in the German “Data Literacy Charter”. Its authors see data ethics as a central component of any set of data-related skills and competencies, to be reflected in all sub-areas of data literacy.
The presentation will discuss how a standard framework for Data and AI Literacy could be structured so that it allows to derive specific competencies and qualifications related to different roles and their activities in data-informed decision-making processes. Such an operational framework can form the basis to design targeted policy interventions, track their progress, and empirically evaluate their outcomes to coordinate global data and AI literacy building efforts. Therefore, I will introduce the term “competence demonstrators” to describe the specific knowledge, skills, and attitudes/values that stakeholders need to use or interact with data and algorithmic systems, in order to reach the intended outcome, including ethical considerations.