Treffer: Ten simple rules for building and maintaining a responsible data science workflow.

This article discusses the importance of incorporating ethics and societal impact considerations into data science research. It outlines 10 rules for building and maintaining a responsible data science workflow, including considering potential harms, questioning data inputs for biases and privacy concerns, and maintaining transparency and diversity in research teams. The article emphasizes the need for transparency, reproducibility, and documentation in data science research, as well as the use of open-source tools for detecting biases and unfairness. It acknowledges the challenges involved in implementing a responsible workflow but highlights the benefits for both the research team and the wider community. [Extracted from the article]