Machine learning algorithms pervade modern life. They shape decisions about who gets a mortgage, who gets a job, and who gets bail, and have become so enmeshed in our political and economic processes that some scientists argue we are witnessing the emergence of “algorithmically infused societies.”
In a new perspective piece for Nature, SFI External Professor Tina Eliassi-Rad and her co-authors* ask how social scientists can investigate algorithmically infused societies, which may require very different methodologies than social sciences have traditionally deployed.
“The existing toolkit of social theories and measurement models was not created with the deep societal reach of algorithms in mind, and may thus not apply to human societies that are permeated by algorithms,” they write. They call attention to major challenges for measuring social phenomena in an algorithmically infused society, and outline five best practices computational social scientists can follow to mitigate "the harmful consequences of (mis)measurement.”
Read the perspective, “Measuring algorithmically infused societies,” in Nature (June 30, 2021)
*External Professor Tina Eliassi-Rad is a member of SFI’s algorithmic justice project, and is based at Northwestern University. Other authors are Claudia Wagner (GESIS Leibniz Institute for the Social Sciences; RWTH Aachen University; Complexity Science Hub Vienna), Markus Strohmaier (GESIS Leibniz Institute for the Social Sciences; RWTH Aachen University; Complexity Science Hub Vienna), Alexandra Olteanu (Microsoft Research), Emre Kiciman (Microsoft Research), and Noshir Contractor (Northwestern University)