TY - JOUR
T1 - An iSchool Approach to Data Science: Human-centered, Socially Responsible, and Context-driven
AU - Shah, Chirag
AU - Anderson, Theresa
AU - Hagen, Loni
AU - Zhang, Yin
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
UR - https://digitalcommons.usf.edu/si_facpub/627
UR - https://doi.org/10.1002/asi.24444
U2 - 10.1002/asi.24444
DO - 10.1002/asi.24444
M3 - Article
VL - 72
JO - Jasist
JF - Jasist
ER -