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Algorithmic rankers have a profound impact on our increasingly data-drivensociety. From leisurely activities like the movies that we watch, therestaurants that we patronize; to highly consequential decisions, like makingeducational and occupational choices or getting hired by companies -- these areall driven by sophisticated yet mostly inaccessible rankers. A small change tohow 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 todeterioration of the prestige of a university or have drastic consequences on ajob candidate who missed out being in the list of the preferred top-k for anorganization. This paper is a call to action to the human-centered data scienceresearch community to develop principled methods, measures, and metrics forstudying the interactions among the socio-technical context of use,technological innovations, and the resulting consequences of algorithmicrankings on multiple stakeholders. Given the spate of new legislations onalgorithmic accountability, it is imperative that researchers from socialscience, human-computer interaction, and data science work in unison fordemystifying how rankings are produced, who has agency to change them, and whatmetrics of socio-technical impact one must use for informing the context ofuse.

Rankers, Rankees, & Rankings: Peeking into the Pandora's Box from a Socio-Technical Perspective