August 8, 2022

Teaching the Craft, Ethics, and Politics of Field Experiments

How can we manage the politics and ethics of large-scale online behavioral research? When this question came up in April during a forum on Defending Democracy at Princeton, Ed Felten mentioned on stage that I was teaching a Princeton undergrad class on this very topic. No pressure!

Ed was right about the need: people with undergrad computer science degrees routinely conduct large-scale behavioral experiments affecting millions or billions of people. Since large-scale human subjects research is now common, universities need to equip students to make sense of and think critically about that kind of power.

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Princeton Dialogues of AI and Ethics: Launching case studies

Summary: We are releasing four case studies on AI and ethics, as part of the Princeton Dialogues on AI and Ethics.

The impacts of rapid developments in artificial intelligence (“AI”) on society—both real and not yet realized—raise deep and pressing questions about our philosophical ideals and institutional arrangements. AI is currently applied in a wide range of fields—such as medical diagnosis, criminal sentencing, online content moderation, and public resource management—but it is only just beginning to realize its potential to influence practically all areas of human life, including geopolitical power balances. As these technologies advance and increasingly come to mediate our everyday lives, it becomes necessary to consider how they may reflect prevailing philosophical perspectives and preferences. We must also assess how the architectural design of AI technologies today might influence human values in the future. This step is essential in order to identify the positive opportunities presented by AI and unleash these technologies’ capabilities in the most socially advantageous way possible while being mindful of potential harms. Critics question the extent to which individual engineers and proprietors of AI should take responsibility for the direction of these developments, or whether centralized policies are needed to steer growth and incentives in the right direction. What even is the right direction? How can it be best achieved?

Princeton’s University Center for Human Values (UCHV) and the Center for Information Technology Policy (CITP) are excited to announce a joint research project, “The Princeton Dialogues on AI and Ethics,” in the emerging field of artificial intelligence (broadly defined) and its interaction with ethics and political theory. The aim of this project is to develop a set of intellectual reasoning tools to guide practitioners and policy makers, both current and future, in developing the ethical frameworks that will ultimately underpin their technical and legislative decisions. More than ever before, individual-level engineering choices are poised to impact the course of our societies and human values. And yet there have been limited opportunities for AI technology actors, academics, and policy makers to come together to discuss these outcomes and their broader social implications in a systematic fashion. This project aims to provide such opportunities for interdisciplinary discussion, as well as in-depth reflection.

We convened two invitation-only workshops in October 2017 and March 2018, in which philosophers, political theorists, and machine learning experts met to assess several real-world case studies that elucidate common ethical dilemmas in the field of AI. The aim of these workshops was to facilitate a collaborative learning experience which enabled participants to dive deeply into the ethical considerations that ought to guide decision-making at the engineering level and highlight the social shifts they may be affecting. The first outcomes of these deliberations have now been published in the form of case studies. To access these educational materials, please see our dedicated website https://aiethics.princeton.edu. These cases are intended for use across university departments and in corporate training in order to equip the next generation of engineers, managers, lawyers, and policy makers with a common set of reasoning tools for working on AI governance and development.

In March 2018, we also hosted a public conference, titled “AI & Ethics,” where interested academics, policy makers, civil society advocates, and private sector representatives from diverse fields came to Princeton to discuss topics related to the development and governance of AI: “International Dimensions of AI” and “AI and Its Democratic Frontiers”. This conference sought to use the ethics and engineering knowledge foundations developed through the initial case studies to inspire discussion on AI technology’s wider social effects.

This project is part of a wider effort at Princeton University to investigate the intersection between AI technology, politics, and philosophy. There is a particular emphasis on the ways in which the interconnected forces of technology and its governance simultaneously influence and are influenced by the broader social structures in which they are situated. The Princeton Dialogues on AI and Ethics makes use of the university’s exceptional strengths in computer science, public policy, and philosophy. The project also seeks opportunities for cooperation with existing projects in and outside of academia.

Ethics Education in Data Science

Data scientists in academia and industry are increasingly recognizing the importance of integrating ethics into data science curricula. Recently, a group of faculty and students gathered at New York University before the annual FAT* conference to discuss the promises and challenges of teaching data science ethics, and to learn from one another’s experiences in the classroom. This blog post is the first of two which will summarize the discussions had at this workshop.

There is general agreement that data science ethics should be taught, but less consensus about what its goals should be or how they should be pursued. Because the field is so nascent, there is substantial room for innovative thinking about what data science ethics ought to mean. In some respects, its goal may be the creation of “future citizens” of data science who are invested in the welfare of their communities and the world, and understand the social and political role of data science therein. But there are other models, too: for example, an alternative goal is to equip aspiring data scientists with technical tools and organizational processes for doing data science work that aligns with social values (like privacy and fairness). The group worked to identify some of the biggest challenges in this field, and when possible, some ways to address these tensions.

One approach to data science ethics education is including a standalone ethics course in the program’s curriculum. Another option is embedding discussions of ethics into existent courses in a more integrated way. There are advantages and disadvantages to both options. Standalone ethics courses may attract a wider variety of students from different disciplines than technical classes alone, which provides potential for rich discussions. They allow professors to cover basic normative theories before diving into specific examples without having to skip the basic theories or worry that students covered them in other course modules. Independent courses about ethics do not necessarily require cooperation from multiple professors or departments, making them easier to organize. However, many worry that teaching ethics separately from technical topics may marginalize ethics and make students perceive it as unimportant. Further, standalone courses can either be elective or mandatory. If elective, they may attract a self-selecting group of students, potentially leaving out other students who could benefit from exposure to the material; mandatory ethics classes may be seen as displacing other technical training students want and need. Embedding ethics within existent CS courses may avoid some of these problems and can also elevate the discourse around ethical dilemmas by ensuring that students are well-versed in the specific technical aspects of the problems they discuss.

Beyond course structure, ethics courses can be challenging for data science faculty to teach effectively. Many students used to more technical course material are challenged by the types of learning and engagement required in ethics courses, which are often reading-heavy. And the “answers” in ethics courses are almost never clear-cut. The lack of clear answers or easily constructed rubrics can complicate grading, since both students and faculty in computer science may be used to grading based on more objective criteria. However, this problem is certainly not insurmountable – humanities departments have dealt with this for centuries, and dialogue with them may illuminate some solutions to this problem. Asking students to complete frequent but short assignments rather than occasional long ones may make grading easier, and also encourages students to think about ethical issues on a more regular basis.

Institutional hurdles can hinder a university’s ability to satisfactorily address questions of ethics in data science. A dearth of technical faculty may make it difficult to offer a standalone course on ethics. A smaller faculty may push a university towards incorporating ethics into existent CS courses rather than creating a new class. Even this, however, requires that professors have the time and knowledge to do so, which is not always the case.

The next blog post will enumerate topics discussed and assignments used in courses that discuss ethics in data science.

Thanks to Karen Levy and Kathy Pham for their edits on a draft of this post.