  Algorithmic rankers have a profound impact on our increasingly data-driven
society. From leisurely activities like the movies that we watch, the
restaurants that we patronize; to highly consequential decisions, like making
educational and occupational choices or getting hired by companies -- these are
all driven by sophisticated yet mostly inaccessible rankers. A small change to
how these algorithms process the rankees (i.e., the data items that are ranked)
can have profound consequences. For example, a change in rankings can lead to
deterioration of the prestige of a university or have drastic consequences on a
job candidate who missed out being in the list of the preferred top-k for an
organization. This paper is a call to action to the human-centered data science
research community to develop principled methods, measures, and metrics for
studying the interactions among the socio-technical context of use,
technological innovations, and the resulting consequences of algorithmic
rankings on multiple stakeholders. Given the spate of new legislations on
algorithmic accountability, it is imperative that researchers from social
science, human-computer interaction, and data science work in unison for
demystifying how rankings are produced, who has agency to change them, and what
metrics of socio-technical impact one must use for informing the context of
use.

Human-Computer Interaction