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

作者：Sara Stoudt, Yacine Jernite, Brandeis Marshall, Ben Marwick, Malvika Sharan, Kirstie Whitaker, Valentin Danchev · 发表于：PLoS Computational Biology · 年份：2024 · DOI：10.1371/journal.pcbi.1012232 · 被引用次数：10 · 研究领域：Scientific Computing and Data Management、Research Data Management Practices、Genetics, Bioinformatics, and Biomedical Research

Contributors and beneficiaries of data-intensive research have become increasingly concerned about social and ethical risks from data science and machine learning applications Instances of unethical use of technology and harms caused to vulnerable communities have made it even more urgent for researchers to broaden the considerations of ethics and societal impact in their research. There has been a proliferation of ethical guidelines [7-10], checklists for responsible research While encouraging, there is also a risk that ethical considerations from guidelines and checklists may be added to a project as an afterthought unless such considerations are incorporated into the research process from the onset so that data science can be performed responsibly by design (in a similar vein as advocated for by Open Science by Design [14]). To help enable this goal of incorporating ethics through the entire research process, we outline 10 simple rules of a responsible data science workflow.