Ensuring transparency in the recruitment and talent acquisition process is essential. However, the current utilization of automated decision systems has proven to be disadvantageous for recruiters. This endeavor represents an initial effort to establish contextual transparency in online recruiting, with a specific emphasis on promoting candidate diversity and highlighting transferrable skills.

Ranking is a socio-technical artifact that humans interact with daily. This work is an effort to develop principled protocols to understand the existing public rankings (e.g., college rankings), domain-specific rankings (e.g., loan applicant rankings), and rankings that are machine-generated (e.g., social media feed rankings.). 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 to demystify 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.