A Mental Models Approach to Ethical Decision-Making

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This project, supported by the cross-NSF program in Ethics Education in Science & Engineering, investigates the ethical decision-making process for graduate students in science, technology, engineering and math (STEM). Graduate students and faculty in STEM areas typically get very little training or education in research ethics. Instead, faculty and students bring intuitive beliefs and implicit frameworks as the basis for the judgments they make in their work. In traditional research ethics training, student discussions of cases of plagiarism or falsification of data tend to be about right-versus-wrong with little consideration of cultural or other contexts. This approach is limited in expanded global scientific networks because consideration of cultural values and morals must extend beyond those that are held by the majority of one culture. To the extent that students' intuitive frameworks include questions not addressed in training, they may have difficulty reconciling their intuitive beliefs with a curriculum that seems to have neglected important considerations. In the complex environments in which today's research is conducted, it is important to understand these cognitive frameworks and address them in teaching ethical decision-making. This project will provide an explicit, systematic approach for determining what concepts ought to be covered in two key domains of graduate research ethics training: privacy and transparency. Privacy is concealing and transparency is revealing. Both complementary domains are important to research interests, and balancing the two is critical to the development of technology and the scientific enterprise itself. This project first develops an expert model on these two ethical issues by soliciting input from practicing researchers with international and interdisciplinary experience in science and technology. Secondly, the mental models held by graduate students in science and technology are elicited, characterized and compared to the expert model. Finally, this comparative method will provide a guide for developing an education module that can bring graduate students' ethical decision-making closer to the expert level.