FUNDAMENTALS OF COMPUTER PROGRAMMING I
Given the role of Fundamentals of Computer Programming I as the first course for all CS majors and many others with an interest in CS, the course aims to develop core skills in critical analysis of ethical issues with an emphasis on self-reflection, finding purpose, and intersectionality. Students are invited to explore different “topic modules” that introduce broad topics such as accountability, bias, or impact, and mandatory modules bookend the quarter that encourage introspection and discussion between students who have attended different topic modules.
DATA STRUCTURES AND ALGORITHMS
The Data Structures and Algorithms course, mandatory for computer science students, should serve as a step toward making diverse and intersectional perspectives heard in the decision making spaces, which drive technological innovation. Our materials prepare students to consider issues beyond their own needs in their program design, which is essential for students pursuing careers in the tech industry.
ELECTRONIC SYSTEM DESIGN I
We took one 80-minute class session to discuss the ethics of embedded systems, centered on one real-world and one hypothetical case study. We chose these case studies based on their richness of ethical ramifications and their emphasis on community assumptions and impact. After critically examining the technology’s ethical implications, students reflected on their agency to align their actions with their values.
NATURAL & ARTIFICIAL VISION
Prof. Emma Alexander highlights one possible framework for considering some of the ethical challenges and possibilities in the bio-inspired computer vision field, meant to be accessible to an interested undergraduate. The materials explore topics such as differentially-valued expertise and labor, the historical context of computer vision design choices, and tools for action (CIDER, local design tools, enforcement mechanisms, etc). Students then reflect on the ethics of bio-inspired computer vision in their final reports.
MACHINE LEARNING
The ethics curriculum will introduce students to the basics of what ethical thinking is in the context of machine learning (ML) by teaching them some basic terms/ideas that would be most relevant to specific models of ML, and providing resources for them to further reflect on the implication of the ethics behind it from their own perspectives. Students could be asked to evaluate real world scenarios, hypothetical scenarios, and their own code using these ethical ideas. To help students figure out how to analyze things meaningfully, examples could be given of existing analysis, like asking students to read a paper or opinion piece.