Related papers: Teaching Responsible Data Science: Charting New Pe…

Teaching applied ethics in computer science has shifted from a perspective of teaching about professional codes of conduct and an emphasis on risk management towards a broader understanding of the impacts of computing on humanity and the…

In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our…

Ethics have become an urgent concern for data science research, practice, and instruction in the wake of growing critique of algorithms and systems showing that they reinforce structural oppression. There has been increasing desire on the…

Data science education is increasingly involving human subjects and societal issues such as privacy, ethics, and fairness. Data scientists need to be equipped with skills to tackle the complexities of the societal context surrounding their…

Data science is an interdisciplinary research area where scientists are typically working with data coming from different fields. When using and analyzing data, the scientists implicitly agree to follow standards, procedures, and rules set…

There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections…

As data science emerges as a distinct academic discipline, introductory data science (IDS) courses have also drawn attention to their role in providing foundational knowledge of data science to students. IDS courses not only help students…

This project addresses the challenges of responsible and fair resource allocation in data science (DS), focusing on DS queries evaluation. Current DS practices often overlook the broader socio-economic, environmental, and ethical…

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the…

Case studies are typically used to teach 'ethics', but when the content of a course is focused on formulae and proofs, a case analysis and the knowledge, skills, and abilities they require can be distracting. Moreover, case analyses are…

The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary…

Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunity for…

As belief around the potential of computational social science grows, fuelled by recent advances in machine learning, data scientists are ostensibly becoming the new experts in education. Scholars engaged in critical studies of education…

Applying Design Science Research (DSR) methodology is becoming a popular working resource for most Information Systems (IS) and Software engineering studies. The research and/or practical design problems that must be faced aim to answer the…

Given the potential for technology to inflict harm and injustice on society, it is imperative that we cultivate a sense of social responsibility among our students as they progress through the Computer Science (CS) curriculum. Our students…

I give a short introduction to data ethics. I begin with some background information and societal context for data ethics. I then discuss data ethics in mathematical-science education and indicate some available course material. I briefly…

This article is concerned with setting up practical guardrails within the research activities and environments of CSS. It aims to provide CSS scholars, as well as policymakers and other stakeholders who apply CSS methods, with the critical…

Ethics in the emerging world of data science are often discussed through cautionary tales about the dire consequences of missteps taken by high profile companies or organizations. We take a different approach by foregrounding the ways that…

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data…

Reproducibility should be a cornerstone of science as it enables validation and reuse. In recent years, the scientific community and the general public became increasingly aware of the reproducibility crisis, i.e. the wide-spread inability…