Kurfess, F., & Vasilaky, K., & Cheuk, T., & Jenkins, R., & Nolan, G., & Hajrasouliha, A., & St John, E. (2022, August), Assessment of Ethics and Social Justice Aspects in Data Science and Artificial Intelligence  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41550

\bibitem{asee_peer_41550}
  Kurfess, F., \& Vasilaky, K., \& Cheuk, T., \& Jenkins, R., \& Nolan, G., \& Hajrasouliha, A., \& St John, E. (2022, August), \emph{Assessment of Ethics and Social Justice Aspects in Data Science and Artificial Intelligence}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--41550

Franz Kurfess, Katya Vasilaky, Tina Cheuk, Ryan Jenkins, Grace Nolan, Amir Hajrasouliha, and Elise St John.  "Assessment of Ethics and Social Justice Aspects in Data Science and Artificial Intelligence".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://peer.asee.org/41550 Internet.  24 Apr, 2025

\bibitem{asee_peer_41550}
  Franz Kurfess, Katya Vasilaky, Tina Cheuk, Ryan Jenkins, Grace Nolan, Amir Hajrasouliha, and Elise St John.
  "Assessment of Ethics and Social Justice Aspects in Data Science and Artificial Intelligence".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://peer.asee.org/41550 Internet.  24 Apr, 2025

@INPROCEEDINGS{asee_peer_41550
author = "Franz Kurfess, Katya Vasilaky, Tina Cheuk, Ryan Jenkins, Grace Nolan, Amir Hajrasouliha, and Elise St John"
title = "Assessment of Ethics and Social Justice Aspects in Data Science and Artificial Intelligence"
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/41550}
number = {10.18260/1-2--41550}
}

TY  - CPAPER
AB  - Abstract
This work aims to develop a set of materials and tools, both quantitative and qualitative, for two purposes: First, for the assessment of ethical and social justice (ESJ) considerations in research projects, and second, as a pedagogical toolkit that allows users to improve their understanding of these aspects of data ethics. Below we describe three existing assessment methodologies for evaluating ESJ in data science research projects: a scoring rubric, a questionnaire, and a canvas sheet (i.e., a user-friendly template and tool that captures data), and we propose one additional method, a predictive machine learning model. This document describes an evaluation of the feedback from 124 students in two different classes who used the questionnaire and canvas sheet to assess their team projects. This data set is also being used to test a proof of concept for the machine learning model. Our emphasis at this stage is to improve the instruments, with a quantitative analysis of the numerical and scale-based responses, and a qualitative evaluation of the text-based suggestions from participants.  The primary insights from this first round of evaluations indicate that students showed no strong preference between the questionnaire and the canvas sheet, with slight advantages on “Perspective” and “Further Research” for the canvas sheet, and a similar advantage for “Group Discussion” for the questionnaire. 
AU  - Franz Kurfess
AU  - Katya Vasilaky
AU  - Tina Cheuk
AU  - Ryan Jenkins
AU  - Grace Nolan
AU  - Amir Hajrasouliha
AU  - Elise St John
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Assessment of Ethics and Social Justice Aspects in Data Science and Artificial Intelligence
UR  - https://peer.asee.org/41550
DO  - 10.18260/1-2--41550
ER  -