Artificial Intelligence: Principles and Techniques

Learning goal: Understanding a professional ethics statement; evaluating real cases using the statement; practicing the professional identity of a responsible AI practitioner.

Description: Students use the NeurIPS machine learning conference ethics guidelines to evaluate four different real-world scenarios. 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.

Contains: Starter Code, Assignment Handout, Explainer

Learning goal: Understand and apply principles of fair distribution to the outcomes of a toxicity classifier; identify and compare ethical concerns with different methods of data collection.

Description: 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.

In Problem 4, students train two classifiers with different objectives: one has lower average loss, while the other has lower maximum group loss.

The ethical reflection questions ask, how should the correct classifications be distributed across commenters? We introduce three well-known principles of fair distribution and ask students to use one in their argument for which classifier should be preferred. A final question asks students to consider the ethics of data collection.

Contains: Starter Code, Assignment Handout, Explainer

Learning goal: connecting modeling choices to their ethical implications; exploring moral duties to current and future people.

Description: Students have created a MDP 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.

Contains: Starter Code, Assignment Handout, Explainer

Learning goal: acknowledging ethical constraints on optimization; identifying and describing values relevant to a real-world scenario.

Description: 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.

Contains: Starter Code, Assignment Handout

Learning goal: understanding concepts of surveillance, dual use technologies, ethics dumping, and foreseeable harms.

Description: 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.

Contains: Starter Code, Assignment Handout, Explainer