This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities.

Participation in democracy, in today’s digital and datafied society, requires the development of a series of transversal skills, which should be fostered in higher education (HE) through critically oriented pedagogies that interweave technical data skills and practices together with information and media literacies. If students are to navigate the turbulent waters of data and algorithms, then data literacies must be featured in academic development programmes, thereby enabling HE to lead in the development of approaches to understanding and analysing data, in order to foster reflection on how data are constructed and operationalised across societies, and provide opportunities to learn from the analysis of data from a range of sources. The key strategy proposed is to adopt the use of open data as open educational resources in the context of problem and research-based learning activities. This paper introduces a conceptual analysis including an integrative overview of relevant literature, to provide a landscape perspective to support the development of academic training and curriculum design programmes in HE to contribute to civic participation and to the promotion of social justice.

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black‐box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision‐making entails conceptual questions: Is explicability a free‐standing principle? Is it already covered by the well‐established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual‐ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to?

Extreme inequality of opportunity leads to a number of social tensions, inefficiencies and injustices. One issue of increasing concern is the effect inequality is having on people’s fair chances of attaining meaningful work, thus limiting opportunities to make a significant positive contribution to society and reducing the chances of living a flourishing life and developing their potential. On a global scale, we can observe an increasingly uneven provision of meaningful work, raising a series of ethical concerns that need detailed examination. The aim of this article is to explore the potential of a normative framework based upon the idea of contributive justice to defend a fairer provision of meaningful work.