Data Science Ethics: Bridging Teaching and Practice

Jyotishka Datta Chair
Virginia Tech
Nicholas Horton Discussant
Amherst College
Samantha Robinson Organizer
University of Arkansas
Tuesday, Aug 6: 8:30 AM - 10:20 AM
Invited Paper Session 
Oregon Convention Center 
Room: CC-255 



Main Sponsor

Section on Statistics and Data Science Education

Co Sponsors

Committee on Membership Retention and Recruitment
Justice Equity Diversity and Inclusion Outreach Group
Section on Teaching of Statistics in the Health Sciences


A Multimodal Approach to Integrating Ethics into an Undergraduate Data Science Curriculum

In the age of generative AI and harmful automation tools that enhance existing inequalities, integrating ethics into our data science curricula is imperative. Statistics and data science professors are often not trained in philosophy and ethics and they lack the necessary background knowledge and skills to give a full treatment to ethics in their courses. This presentation discusses different approaches to integrating data science ethics into a statistics and data science curriculum. Partnering with philosophy/ethics departments on campus, one approach might be to create modules where ethics sections of courses could be mixed and matched. This work could be supported over the summer. Another approach may be to re-think our majors, requiring an ethics course. Perhaps an all-of-the above approach is best. The pros and cons of these various approaches, as well as implications for staffing and retraining are discussed.  


Randi Garcia, Smith College

Data Ethnography: Cultivating Reflexive Sensibilities through the Cultural Analysis of Datasets

Canonical ideologies tend to position datasets as neutral representational tools, when datasets may be more aptly characterized as power-laden systems for signification. While critical for interpreting the cultural meaning of data, the skills needed to historicize, situate, and deconstruct datasets are often underrepresented in STEM education. In this talk, I outline a series of pedagogical approaches to teaching cultural analysis of datasets. I show how, by cultivating competency in hermeneutics, ethnography, and critical theory, students can learn to attend to the cultural provenance of datasets across a number of registers – from interrogating the belief systems of data designers, to examining the cultural logics of data infrastructures, to analyzing the interests of data-producing institutions, to unpacking the discourses that shape public understandings of data. Further, by pluralizing the epistemic lenses through which data are analyzed, students have an opportunity to nourish reflexive sensibilities – discerning their own cultural positioning as they question why culture tends to be deleted from data science work. 


Lindsay Poirier

Integrating Ethics into GAISE

Statistics education at all levels includes data collected on human subjects. Thus, statistics educators have a responsibility to educate their students about the ethical aspects related to the collection of those data. With technological advancement and the increase in availability of real-world datasets, it is necessary that instructors educate about integrating the ethical aspects around data sources, such as privacy, how the data were obtained and whether participants consent to the use of their data. In this talk, we propose incorporating ethics into established curricula and integrating ethics into undergraduate-level introductory statistics courses based on recommendations in the GAISE Report. We provide a few examples of how to prompt students to constructively think about their ethical responsibilities when working with data.


Matt Hayat, Georgia State University

Philosophy as Integral to a Data Science Ethics Course

There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy. Here, we present a framework for bringing together key data science practices with ethical topics. The ethical topics were collated from sixteen data science ethics courses with public-facing syllabi and reading lists. We encourage individuals who are teaching data science ethics to engage with the philosophical literature and its connection to current data science practices, which is rife with potentially morally charged decision points. 


Johanna Hardin, Pomona College

The Philosopher Crossover: Benefits of Collaboration when Developing a Data Science Ethics Course

While undergraduate and graduate programs in statistics, biostatistics, data science, and related areas provide students ample opportunity to develop the skills necessary for data driven work post-graduation, many have recognized the need and the responsibility for these programs to incorporate ethical thinking into their curriculum. While some programs still lack coursework related to such topics, many are either developing their own data ethics courses or requiring already existent ethics courses offered from philosophy departments. In this presentation, we focus on a philosopher-statistician partnership and the resulting collaborative development of a data ethics course at one major institution. We illustrate how such a course can be developed, we describe the course itself, and, in particular, we discuss the benefits of partnering with philosophers when developing data ethics courses and how this philosopher crossover has helped students (and faculty) delve deeper into contextualized data ethics considerations than would have been possible without the collaborative effort. 


Samantha Robinson, University of Arkansas