Machine Learning Times
Machine Learning Times
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3 years ago
How Not to Know Ourselves

Originally published in Medium, July 29, 2020.

Platform data do not provide a direct window into human behavior. Rather, they are direct records of how we behave under platforms’ influence.

Surfing a wave of societal awe and excitement about “Big Data,” platforms formed a habit of releasing “data science” insights on what we search, like, express, purchase, obsess over, attempt to hide, and prefer to forget. These colorful graphics and juicy taglines — most notably from OKCupid and PornHub, whose data lay claims to the quirks and desires of our intimate lives — are always popular novelties to behold, ponder, and reference. If knowing ourselves through platform data is a practice of our age, it is certainly not confined to platforms themselves. Aspiring data scientists, curious programmers, vigilant data journalists, analysts of civic organizations and political campaigns, and (last but not the least) academic social scientists such as myself make up the growing field that is figuring out who we are, what we do, and how we sway in the swathes of platform data.

Such data can be impressive due to their unprecedented granularity and volume, as well as the fact that they are seemingly “unobtrusive” recordings of our activities when no one is watching. These apparent strengths of data for social research are outweighed by a problem in what we call the “measurement conditions”: platform data are platforms’ records of their own behavioral experimentation. Trying to know ourselves through platform data tends to yield partial and contorted accounts of human behavior that conceal platform interventions. Moreover, though increasingly produced by non-corporate actors, such knowledge accounts and narratives tend to be amenable to platform money-making and image-building.

Trying to know ourselves through platform data tends to yield partial and contorted accounts of human behavior that conceal platform interventions.

To be clear, for years many have contested the ascendance of platform data as a staple in quantitative social sciences alongside conventional data collection methods, such as surveys and experiments. These contestations focus on issues about the data’s representativeness, privacy concerns, and precarious access at the mercy of platform companies. The “measurement conditions” problem, however, is entirely different. In our newly published paper, Harsh Taneja and I call for attention to the circumstances under which these data come about: what purpose does the measurement initially serve? As historians have told us, measurement — or converting parts of the social world into quantities according to some enduring instrument — is not an end in itself, but a means for managing events and coordinating actions.  Measurement is thus a product of the social and institutional context (i.e., “measurement conditions”) in which it is called upon and carried out. 

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