An iSchool approach to data science: Human‐centered , socially responsible, and context‐driven

作者：Chirag Shah, Theresa Dirndorfer Anderson, Loni Hagen, Yin Zhang · 发表于：Journal of the Association for Information Science and Technology · 年份：2021 · DOI：10.1002/asi.24444 · 被引用次数：29 · 研究领域：Ethics and Social Impacts of AI、Privacy-Preserving Technologies in Data、Mobile Crowdsensing and Crowdsourcing

Abstract The Information Schools, also referred to as iSchools, have a unique approach to data science with three distinct components: human‐centeredness, socially responsible, and rooted in context. In this position paper, we highlight and expand on these components and show how they are integrated in various research and educational activities related to data science that are being carried out at iSchools. We argue that the iSchool way of doing data science is not only highly relevant to the current times, but also crucial in solving problems of tomorrow. Specifically, we accentuate the issues of developing insights and solutions that are not only data‐driven, but also incorporate human values, including transparency, privacy, ethics, fairness, and equity. This approach to data science has meaningful implications on how we educate the students and train the next generation of scholars and policymakers. Here, we provide some of those design decisions, rooted in evidence‐based research, along with our perspective on how data science is currently situated and how it should be advanced in iSchools.