Roundtable on Data Science Postsecondary Education A Compilation of Meeting Highlights (2020) / Chapter Skim
Currently Skimming:
6 Meeting #5: Integrating Ethical and Privacy Concerns into Data Science Education
6 Meeting #5: Integrating Ethical and Privacy Concerns into Data Science Education
Pages 62-77
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 62... ...
Welcoming roundtable participants, co-chair Eric Kolaczyk, Boston University, noted that there are inherent ethical and privacy implications in the choices data scientists make while framing, obtaining, cleaning, manipulating, and interpreting data. He highlighted the value of integrating this context of data science practice into data science education, and he hoped that the conversations at this gathering of the roundtable would contribute to a more principled awareness of the ethics of data science.
|
From page 63... ...
She encouraged academicians to address this issue of accountability in data science classrooms. She advocated for exposing future data scientists to these problems and teaching them to see themselves as accountable for ethically responsible products.
|
From page 64... ...
She noted the value of having a Hippocratic Oath for data science and encouraged data scientists to focus on their roles as translators of ethics instead of arbiters of truth. In response to a question from Solon Barocas, Cornell University, she suggested that data scientists reject jobs with organizations that do not build ethical (and legal)
|
From page 65... ...
Similarly, common professional virtues to strive to instill within future data scientists include skepticism about how models will perform, humility regarding the limits of the models that one develops, honesty to avoid misleading users, and vigilance to ensure that models work well after deployment. Standard ethical dilemmas can motivate students to question and develop their own moral agency and moral intuition.
|
From page 66... ...
and that data science e thics education can engage students in deliberation about the ethical implications associated with their modeling decisions. Regarding differences in outcomes, Barocas suggested that data scientists consider the historical events that shape algorithmic outputs about an individual (e.g., whether that person's family has a history of interaction with the criminal justice system)
|
From page 67... ...
Barocas closed his presentation by appealing to senior data scientists to lead by example in refusing ethically questionable projects, which in turn will provide an example and protection for more junior researchers, practitioners, and students wishing to reject a project. RECOGNIZING AND ANALYZING FALSE CLAIMS FROM BIG DATA Jevin West, University of Washington West opened his presentation by noting that while many students excel in the execution of mechanics, they often lack the skills both to engage with ethical considerations for data analysis and to understand basic experimental design.
|
From page 68... ...
Be wary of unfair comparisons. West concluded by emphasizing the value of improving ethical data science education m odels at the secondary and postsecondary levels and engaging students and the broader public in data reasoning.
|
From page 69... ...
Alfred Hero, University of Michigan, cautioned that although flagging false claims can energize students, it risks showing students that finger- pointing is always justified. Instead, Hero suggested teaching students to ask what evidence would be needed to make a true claim.
|
From page 70... ...
He emphasized addressing key concepts early and often in courses and encouraged the building of critical thinking skills at different levels. He urged faculty to identify learning outcomes related to data integration and data fusion and suggested enhanced faculty training.
|
From page 71... ...
She emphasized that historical and contextual information are essential in ethical decision making. Hoffmann observed that data ethics is the intersection of moral, methodological, and practical concerns -- data scientists need appropriate tools to balance these three areas.
|
From page 72... ...
Roth clarified that the analyst does not set the privacy level and added that differential privacy is only a metric. He mentioned that there have not been many successful markets for private data in big data applications thus far because they are not very useful and are easily replaceable.
|
From page 73... ...
He asked whether the latter approach is dangerous because it allows a decision to be made after seeing the trade-offs -- in other words, sacrificing privacy and fairness for accuracy. Roth explained that it is the responsibility of the technologists to identify trade-offs and of the society to balance competing needs.
|
From page 74... ...
Referencing Stodden's earlier observation about bias, Hoffmann recognized that communities contextualize bias differently -- social theory and historical casework can orient people toward a positive vision about a socially acceptable definition. Mark Krzysko, Department of Defense, mentioned that his team regularly confronts many of the issues discussed in Hoffmann's presentation.
|
From page 75... ...
An audience participant suggested developing a required ethics course, separate from core requirements and including guest lecturers from other departments, as a way to motivate students to think about the consequences of working with data. This participant also suggested adding a question about real-world consequences to every student project.
|
From page 76... ...
So that students do not become paralyzed with skepticism, it is important that these case studies show students possible solutions to problems. Because undergraduate students are often interested in exploring problems and nontechnical material, it may seem more feasible to incorporate ethics at this level than at the master's level, in which students are focused on building technical skills that can be applied in the workforce.
|
From page 77... ...
The course invites speakers from Brown's School of Public Health to present real data sets and case studies. He will provide a template for privacy and ethical considerations around which speakers will organize their presentations.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.