CSCA 5204: Current Issues in Ethics and AI
CSCA 5204: Current Issues in Ethics and AI
- Course Type: Breadth (MS-AI) Elective (MS-CS)
- Specialization:Artificial Intelligence Ethics Specialization
- Instructor:ÌýDr. Bobby Schnabel, Professor of Computer Science,ÌýDepartment External Chair
- Prior knowledge needed:
- Programming languages: N/A
- Math: N/A
- Technical requirments: N/A
- A basic experience level with western philosophic and ethical frameworks
Learning Outcomes
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
Course Grading Policy
| Assignment | Percentage of Grade | AI Usage Policy |
|---|---|---|
| Quizzes (5) | 25% (5% each) | Conditional |
| Peer Reviews (4) | 40% (10% each) | Conditional |
| Final Project | 35% | Conditional |
Course Content
Duration: 6 hours
In the first module of this course introduces you to the topic of current ethical issues in AI from several perspectives. After providing an overview of the course including the learning objectives and what work is required of you, you’ll gain insights into key ethical theories that will underlie discussions throughout the course. These include Kantianism, Virtue Ethics, Utilitarianism, and Social Contract Theory. Then we’ll further motivate the course by looking at key ethical concerns that have been identified related to the uses of AI, and the range and pace of AI development and use.
Learning Objectives
- Reflect upon why consideration of ethical issues is a crucial part of the study of AI.
- Describe the core principles and ideas behind Kantianism, Virtue Ethics, Utilitarianism, and Social Contract Theory.
- Apply ethical frameworks to analyze real-life scenarios related to computing and AI.
Duration: 6Ìýhours
In this module, we look at fundamental ethical issues in AI: fairness, bias, and accuracy of algorithms based upon AI. Our lessons focus on three main topics: identity-related bias in large language models; bias in large language models related to images, both image generation and facial recognition; and misinformation and privacy issues in large language models. Through discussions and your independent assessment of current articles, you will gain an understanding of core ethical issues related to generative AI and large language models.
Learning Objectives
- Identify the ways that different forms of algorithmic bias occur in AI algorithms.
- Describe the privacy issues that arise in generative AI and AI applications.
- Apply ethical principles to real-world scenarios involving algorithmic bias, misinformation, and privacy in AI.
Duration: 6Ìýhours
In this module and the next, we switch from broad, general discussion of ethical issues associated with AI, to discussions focused on specific, important sectors. The first of those, which we cover in this module, is one of most important sectors for all people, healthcare. Our coverage includes a brief overview of the myriad uses of AI in healthcare, discussion of a few of many general ethical issues related to uses of AI in healthcare, and a particularly intriguing specific topic, the nascent field of neural implants. The discussions, readings, and an interview assignment will provide you with perspectives on this topic.
Learning Objectives
- Identify the range of ethical issues that arise in the uses of AI in healthcare.
- Analyze the ethical challenges posed by neural implants, considering their benefits and risks.
- Analyze and evaluate ethical dilemmas associated with current and potential future uses of neural implants and the uses of AI in healthcare overall.
Duration: 4Ìýhours
In this module, we explore a very broad area of AI which has great importance and leads to many compelling ethical issues, robotics. The applications of robotics are diverse, and we explore issues associated with three different uses: in healthcare, in policing and the justice system, and in warfare. As is the case throughout the course, our readings reflect very recent developments and applications. The material covered in these lessons is enhanced by a report you will create in an article that pertains to either this topic or to the use of AI in healthcare.
Learning Objectives
- Analyze the ethical dimensions of robotics in healthcare, including its use in medical procedures and for companionship.
- Identify and assess the range of ethical issues that arise when using robotics in policing and in warfare.
- Apply ethical principles to analyze the wide range of uses of robotics in healthcare, policing, and warfare.
Duration: 8 hours
In this final module of the course, we explore our broadest topic and a very interesting one: the societal impacts of AI. There are many topics in this category, and we explore four of them: the prospects and risks of artificial general intelligence; how AI should be regulated; the energy implications of AI, both negative and positive; and the implications of AI for the future of human work and employment. We sample reading about these topics, each of which could be a course by itself, and reinforce what we have learned by formulating our opinions on one of these topics and then having a conversation on the topic with an AI chatbot. We conclude with a brief course wrap-up which includes discussion of how AI professionals should deal with ethical considerations, in the workplace and generally.
Learning Objectives
- Articulate the concepts of achieving artificial general intelligence and the risks and ethical issues associated with it.
- Identify the ethical and societal issues associated with the energy costs and potential energy benefits related to AI.
- Discuss the ethical and societal considerations associated with AI’s influence on the availability and types of jobs, and on workforce dynamics.
Duration: 2 hours
Format: Peer Reviewed Assignment
The final project is a Peer Review. There are 6 prompts. Note that 5 of the prompts are questions from the module quizzes. You may reuse the answers you provided in the quizzes; however, if you do, please revise and expand on them.
You will have 2 attempts to submit your assignment. You will conduct a peer review of 3 reports from your classmates.
Notes
- Cross-listed Courses: CoursesÌýthat are offered under two or more programs. Considered equivalent when evaluating progress toward degree requirements. You may not earn credit for more than one version of a cross-listed course.
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