Computational Social Science: Balancing Ethics and Data-Driven Research in Academia

Innovation Should Start with a Culture of Responsibility

In a paper recently published by Science, Harold F. Linder Professor Alondra Nelson and her co-authors address ethical concerns related to the methods of computational social science (CSS) and the optimal conditions for the utilization of data-based research in the university setting.

This multidisciplinary view provides a roadmap for navigating the potential pitfalls of working with big data sets; respecting privacy and human lives behind the data; and encouraging research that offers equitable benefits to society.

In order to build an ethical framework that achieves these goals, there is a need to bring stakeholders together. According to the research team:

Often missing in the public debates are voices for policies that would encourage or mandate the ethical use of private data that preserve public values like privacy, autonomy, security, human dignity, justice, and balance of power to achieve important public goals—whether to predict the spread of disease, shine a light on societal issues of inclusion, diversity equity and access, or the collapse of the economy. Also, often missing are investments in infrastructures in the academy that could both power knowledge production and maintain privacy.

One aspect of Nelson’s research focuses on emerging scientific and technological phenomena and confronting the social impacts that come as result. She has produced several major publications on direct-to-consumer genetic testing and works with experts across disciplines to shed light on issues of race, science, technology, and social inequality.

During her plenary lecture “The Social Life of DNA and the Need for a New Bioethics” delivered at the 2020 annual meeting of the American Association for the Advancement of Science (AAAS), Nelson cites several examples of the unintended and unconditional dissemination of genetic data, making the case for new bioethical standards and regulatory “guardrails” in genetic research. Read more at AAAS.

Nelson similarly brings a broad perspective and critical analysis to the accelerating field of CSS. In a separate March 2017 paper for PLOS Computational Biology, Nelson and her co-authors offer “Ten simple rules for responsible big data research.”

Between these two papers, Nelson and her collaborators bring a forward-looking approach to how data is collected, shared, and utilized, and whom these research processes may benefit and harm. Their recommendations illustrate how social awareness and collaboration are as important to the data science revolution as the technological methods themselves.

Read more at Science.


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