Ethics Education in Data Science

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

There is common agreement that data science ethics should be taught, but less consensus about what its objectives should be or how they should be pursued. As the field is so promising, there is considerable room for groundbreaking thinking about what data science ethics ought to mean. In some respects, its goal may be the formation of “future citizens” of data science who are invested in the welfare of their communities and the world, and comprehend the social and political role of data science therein. However there are other models, too: for instance, an alternative goal is to equip aiming data scientists with technical tools and organizational procedures for doing data science work that supports social values (like privacy and fairness). The group worked to recognize some of the biggest challenges in this field, and when possible, some techniques to address these tensions.

One approach to data science ethics education is incorporating a standalone ethics course in the program’s curriculum. Another choice is embedding discussions of ethics into current courses in a more incorporated way. There are advantages and disadvantages to both choices. Standalone ethics courses may attract a wider variety of students from different disciplines than technical classes alone, which offers potential for rich discussions. They allow professors to cover fundamental normative theories before diving into definite examples without having to avoid the elementary theories or worry that students covered them in other course modules. Independent courses about ethics do not essentially require cooperation from numerous professors or departments, making them easier to conduct. However, many worry that teaching ethics distinctly from technical topics may sideline ethics and make students perceive it as irrelevant. Further, standalone courses can either be optional or mandatory. If optional, they may entice a self-selecting group of students, possibly leaving out other students who could benefit from introduction to the material; obligatory ethics classes may be seen as relocating other technical training students want and need. Implanting ethics within existent CS courses may avoid some of these issues and can also elevate the dialog 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 illume some solutions to this problem. Asking students to complete regular but short assignments rather than random long ones may make grading easier, and also inspires students to think about ethical problems on a more regular basis.

Institutional obstacles can hinder a university’s ability to suitably address questions of ethics in data science. A shortage of technical faculty may make it difficult to offer an individual data science course on ethics. A smaller faculty may push a university towards integrating ethics into current CS courses rather than creating a new class. Even this, however, needs that professors have the time and knowledge to do so, which is not always the case.

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