Embedding Ethics in Computer Science

Course: Intro to Systems

This assignment is about assembly, reverse engineering, security, privacy and trust. An earlier version of the assignment by Randal Bryant & David O'Hallaron (CMU), [accessible here](http://csapp.cs.cmu.edu/public/labs.html), used the framing story that students were defusing a ‘bomb’.

Course: Intro to Systems

The assignment is the first in an introduction to systems course. It covers bits, bytes and overflow, continuing students’ introduction to bitwise and arithmetic operations. Following the saturating arithmetic problem, we added a case study analysis about the Ariane-5 rocket launch failure. This provided students with a vivid illustration of the potential consequences of overflows as well as an opportunity to reflect on their responsibilities as engineers. The starter code is the full project provided to students.

Course: Probability

This assignment uses the tools of probability theory to introduce students to _risk weighted expected utility_ models of decision making. The risk weighted expected utility framework is then used to understand decision-making under uncertainty in the context of climate change. Which of the IPCC’s forecasts should we use? Do we owe it to future people to adopt a conservative risk profile when making decisions on their behalf? The assignment also introduces normative principles for allocating responsibility for addressing climate change. Students apply these formal tools and frameworks to understanding the ethical dimensions of climate change.

Course: Probability

This assignment builds on introductory knowledge of machine learning techniques, namely the naïve Bayes algorithm and logistic regression, to introduce concepts and definitions of algorithmic fairness. Students analyze sources of bias in algorithmic systems, then learn formal definitions of algorithmic fairness such as independence, separation, and fairness through awareness or unawareness. They are also introduced to notions of fairness that complicate the formal paradigms, including intersectionality and subgroup analysis, representation, and justice beyond distribution.

Course: Intro to Systems

This lab, the last of the course, asks students to discuss the case of Therac-25, a medical device that delivered lethal radiation due to a race condition.

Course: Intro to Systems

This assignment is about void * and generics. We added a case study about responsible disclosure and partiality. Students read a summary of researcher Dan Kaminsky’s discovery of a DNS vulnerability and answer questions about his decisions regarding disclosure of vulnerabilities as well as their own thoughts on partiality. The starter code is the full project provided to students.

Course: Intro to Systems

When functions have assumptions, limitations or flaws, it is vital that the documentation makes those clear. Without documentation, developers don’t have the information they need to make good decisions when writing their programs. We added a documentation component to this C string assignment. Students write a manual page for the skan_token function they have implemented, learning responsible documentation practice as they go. The starter code is the full project provided to students.

Course: Intro to HCI

The lecture covers topics associated with power relations, the use of language, standpoint and inclusion as they arise in the context of design discovery.

Course: Intro to HCI

The lecture presents the concept of values in design. It introduced the distinction between intended and collateral values; discusses the importance of assumptions in the value encoding process; and presents three strategies to address value conflicts that arise as a result of design decisions.

Course: Intro to HCI

This assignment asks students to consider what values are encoded in their product and the decisions they make in the design process; whether there are conflicting values; and how they address existing value conflicts.

Course: AI Principles

After successfully creating a component of a self-driving car – a (virtual) sensor system that tracks other surrounding cars based on noisy sensor readings – students are prompted to reflect on ethical issues related to the creation, deployment, and policy governance of advanced technologies like self-driving cars. Students encounter classic concerns in the ethics of technology such as surveillance, ethics dumping, and dual-use technologies, and apply these concepts to the case of self-driving cars.

Course: AI Principles

In Problem 3 of this assignment, “Ethical Issue Spotting,” students explore the ethics of four different real-world scenarios using the ethics guidelines produced by a machine learning research venue, the NeurIPS conference. Students write a potential negative social impacts statement for each scenario, determining if the algorithm violates one of the sixteen guidelines listed in the NeurIPS Ethical Guidelines. In doing so, they practice spotting potential ethical concerns in real-world applications of AI and begin taking on the role of a responsible AI practitioner.

Course: Intro to HCI

This assignment asks students to evaluate their peers’ projects through a series of heuristics and to respond to others’ evaluations of their projects. By incorporating ethics questions to this evaluation, we prompt them to consider ethical aspects as part of a product’s design features which should be evaluated alongside other design aspects.

Course: Intro to HCI

This assignment asks students to consider what values are encoded in their product and the decisions they make in the design process; whether there are conflicting values; and how they address existing value conflicts.

Course: AI Principles

This assignment is about Markov Decision Processes (MDPs). In Problem 5, we use the MDP the students have created to model how a coastal city government’s mitigation choices will affect its ability to adapt to rising sea levels over the course of multiple decades. At each timestep, the government may choose to invest in infrastructure or save its surplus budget. But the amount that the sea will rise is uncertain: each choice is a risk. Students model the city’s decision-making under two different time horizons, 40 or 100 years, and with different discount factors for the well-being of future people. In both cases, they see that choosing a longer time horizon or a smaller discount factor will lead to more investment now. Students then are introduced to five ethical positions on the comparative value of current and future generations’ well being. They evaluate their modeling choices in light of their choice of ethical position.

Course: Intro to HCI

With this assignment, students will reflect on the group of users their project is intended to serve; their reasons for selecting these users; the notion of an “extreme user;” and the reason why their perspectives are valuable for the design process. I also asks them to reflect on what accommodations they make for their interviewees.

Course: Intro to HCI

With this assignment, students are prompted to reflect on how proposed solutions to the problems they identify may exclude members of certain communities.

Course: AI Principles

In this assignment, students explore constraint satisfaction problems (CSP) and use backtracking search to solve them. Many uses of constraint satisfaction in real-world scenarios involve assignment of resources to entities, like assigning packages to different trucks to optimize delivery. However, when the agents are people, the issue of fair division arises. In this question, students will consider the ethics of what constraints to remove in a CSP when the CSP is unsatisfiable.

Course: AI Principles

Although each of the problems in the problem set build on one another, the ethics assignment itself begins with Problem 4: Toxicity Classification and Maximum Group Loss. Toxicity classifiers are designed to assist in moderating online forums by predicting whether an online comment is toxic or not so that comments predicted to be toxic can be flagged for humans to review. Unfortunately, such models have been observed to be biased: non-toxic comments mentioning demographic identities often get misclassified as toxic (e.g., “I am a [demographic identity]”). These biases arise because toxic comments often mention and attack demographic identities, and as a result, models learn to _spuriously correlate_ toxicity with the mention of these identities. Therefore, some groups are more likely to have comments incorrectly flagged for review: their group-level loss is higher than other groups.