New Undergraduate Cornell Clinic Researches Automated Scoring Systems

When typing a simple Google search on the Internet, a vast array of systems have the power to decide which links or topics occupy the coveted top results page. However, automated scoring systems may contain unconscious bias due to a variety of factors — system designers may bring their personal bias when designing algorithms or the data sets used for machine learning may already contain bias. The proliferation of rating or ranking systems in everyday life often leads to complaints of online misrepresentation. Cornell’s Due Process Clinic, an undergraduate “clinical” course designed to understand automated scoring systems such as credit scores and search engine rankings, started sending student researchers to collect qualitative data and build their own case studies on these systems.

Clinic director, Prof. Malte Ziewitz, science and technology studies, founded the clinic because he wanted to use legal frameworks to research non-legal situations. The clinic tries to assess how someone who would not have access to a public relations expert could deal with online backlash or poor reviews, hoping to understand the consequences of those who have been misrepresented or ranked improperly.