May 27, 2018

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.

Comments

  1. is the meaning of data science in this post including AI domain too?

  2. Samantha Ahern says:

    I think there needs to be a combination of both compulosry stand alone ethics modules and embedded ethics discussions within technical classes. We can see the huge impact technology has on society to its benefit and detriment. It is important for those developing these technologies to have an understanding of the implications of design decisions and development of technologies. Where possible these discussions should include wider student groups that just CS and Data Scientists.