ethnography is not the only social science tool for algorithmic impact assessment

Quickly responding to Selbst, Elish, and Latonero’s “Accountable Algorithmic Futures“, Data and Society’s response to the Algorithmic Accountability Act of 2019…

The bill would empower the FTC to do “automated decision systems impact assessment” (ADSIA) of automated decision-making systems. The article argues that the devil is in the details and that the way the FTC goes about these assessments will determine their effectiveness.

The point of their article, which I found notable, is to assert the appropriate intellectual discipline for these impact assessments.

This is where social science comes in. To effectively implement the regulations, we believe that engagement with empirical inquiry is critical. But unlike the environmental model, we argue that social sciences should be the primary source of information about impact. Ethnographic methods are key to getting the kind of contextual detail, also known as “thick description,” necessary to understand these dimensions of effective regulation.

I want to flag this as weird.

There is an elision here between “the social sciences” and “ethnographic methods” here, as if there were no social sciences that were not ethnographic. And then “thick description” is implied to be the only source of contextual detail that might be relevant to impact assessments.

This is a familiar mantra, but it’s also plainly wrong. There’s many disciplines and methods within “the social sciences” that aren’t ethnographic, and many ways to get at contextual detail that does not involve “thick description”. There is a worthwhile and interesting intellectual question: what are the appropriate methods for algorithmic impact assessment. The authors of this piece assume an answer to that question without argument.