TY - GEN
T1 - Teaching Responsible Data Science
AU - Stoyanovich, Julia
N1 - Funding Information:
The work discussed here would not have been possible without collaboration and input from many of my colleagues. Falaah Arif Khan, a talented artist and data scientist, is the driving force behind the comic books that have become a distinguishing feature of my teaching and, even more so, of how I think about and communicate complex socio-technical topics. Dr. George Wood, who holds a Ph.D. in Sociology and studies inequalities in public health and criminal justice, has been co-teaching RDS with me at NYU since Spring 2021, bringing the much-needed social science perspective to the course. Dr. Armanda Lewis, an education researcher, has been instrumental in connecting instructional methodologies for RDS with educational best-practices and developing assessment for components of the course. Dr. Debbie Yuster has been teaching RDS as "Ethics for Data Science" at Ramapo College since Spring 2021, and has helped me learn how to transfer the material across institutions and audiences. Dr. Eric Corbett holds a Ph.D. in Digital Media and studies civic technology, community engagement and technical interaction design. He and I collaborated closely on the design of the We are AI public education course. Dr. Mona Sloane, Lucas Rosenblatt, Lucius Bynum, Meghan McDermott, Becky Margraf, Grif Peterson, Jeffrey Lambert, Sadie Coughlin-Prego, and Kaven Vohra all contributed to the development and refinement of the materials and methodologies used in this course. This research was supported in part by NSF Awards No. 1934464, 1916505, and 1922658.
Funding Information:
This research was supported in part by NSF Awards No. 1934464, 1916505, and 1922658.
Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/6/12
Y1 - 2022/6/12
N2 - Responsible Data Science (RDS) and Responsible AI (RAI) have emerged as prominent areas of research and practice. Yet, educational materials and methodologies on this important subject still lack. In this paper, I will recount my experience in developing, teaching, and refining a technical course called "Responsible Data Science", which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. I will also describe a public education course called "We are AI: Taking Control of Technology"that brings these topics of AI ethics to the general audience in a peer-learning setting. I made all course materials are publicly available online, hoping to inspire others in the community to come together to form a deeper understanding of the pedagogical needs of RDS and RAI, and to develop and share the much-needed concrete educational materials and methodologies.
AB - Responsible Data Science (RDS) and Responsible AI (RAI) have emerged as prominent areas of research and practice. Yet, educational materials and methodologies on this important subject still lack. In this paper, I will recount my experience in developing, teaching, and refining a technical course called "Responsible Data Science", which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. I will also describe a public education course called "We are AI: Taking Control of Technology"that brings these topics of AI ethics to the general audience in a peer-learning setting. I made all course materials are publicly available online, hoping to inspire others in the community to come together to form a deeper understanding of the pedagogical needs of RDS and RAI, and to develop and share the much-needed concrete educational materials and methodologies.
KW - Responsible AI
KW - Responsible Data Science
UR - http://www.scopus.com/inward/record.url?scp=85133182558&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133182558&partnerID=8YFLogxK
U2 - 10.1145/3531072.3535318
DO - 10.1145/3531072.3535318
M3 - Conference contribution
AN - SCOPUS:85133182558
T3 - Proceedings of the 1st ACM SIGMOD International Workshop on Data Systems Education: Bridging Education Practice with Education Research, DataEd 2022
SP - 4
EP - 9
BT - Proceedings of the 1st ACM SIGMOD International Workshop on Data Systems Education
A2 - Aivaloglou, Efthimia
A2 - Fletcher, George
A2 - Miedema, Daphne
PB - Association for Computing Machinery, Inc
T2 - 1st ACM SIGMOD International Workshop on Data Systems Education: Bridging Education Practice with Education Research, DataEd 2022, co-located with the ACM SIGMOD Conference
Y2 - 17 June 2022
ER -