Ethics of Computational Research

Session Goals

To understand basic ethical issues surrounding computational methods
To articulate ethical pathways for conducting computational research especially in the Global South
To apply knowledge of the pitfalls of computational methods in considering the ethics of an original research project and broader lived trajectory as a (critical) social scientist

Computational methods, sometimes invoked under the gamut of ‘big data’, have been hailed for their powerful ability to analyze data with large volume, variety, and velocity. Yet precisely for these reasons, computational research also risks overstating its analytical and ethical scope.
Key to ethical knowledge production with computational methods is critical reflection on the relationships between the data studied and the social contexts in which it is embedded. Exceeding the sphere of knowledge production, it must be recognized that computational research also shapes the social conditions it examines. Ethics, in this case, thus also encompasses how knowledge production through computational methods reinforces or transforms extant power relations.
For social scientists, computational methods may represent a significant frontier for knowledge production. But these promises do not come without pitfalls. Understanding the ethical quandaries associated with computing and cultivating the commitment to engaging them constitute new and necessary domains for the project of the critical social sciences in contemporary times.

(~10 sentences) Consider your research project. Using the ideas of Tufekci (2014) and boyd and Crawford (2012), provide and explain one example each of (a) an invalid inference from your data, and (b) an unethical application of your results. What are you able or unable to do to mitigate these inferences and applications?
(~10 sentences) Using the ideas of Abebe and colleagues (2020), how might your research exemplify the role/s of computing as ‘diagnostic’, ‘formalizer’, ‘rebuttal’, or ‘synecdoche’? Drawing on Montiel and Uyheng (2021), how are these orientations to social change shaped by a critical text analytics commitment to collective ontologies, naturalistic epistemologies, socio-politically sensitive ethics, and reflexivities from the margins? Taking these points together, how does your work advance a ‘critical’ orientation to social science?

Abebe, R., Barocas, S., Kleinberg, J., Levy, K., Raghavan, M., & Robinson, D. G. (2020). Roles for computing in social change. In Conference on Fairness, Accountability, and Transparency (pp. 252-260). https://doi.org/10.1145/3351095.3372871

boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878

Montiel, C. J., & Uyheng, J. (2021). Foundations for a decolonial big data psychology. Journal of Social Issues. Advance online publication. https://doi.org/10.1111/josi.12439

Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity, and other methodological pitfalls. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (pp. 505-514). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewPaper/8062