of AA in both summative and formative scenarios (e.g. high-stakes testing and classroom 
instruction, respectively), it is important to ensure fairness in these AA systems and all test-
takers are treated fairly, especially for making high-stakes decisions like college admissions, 
employment, or visa applications. Recently, there has been increasing interest in AA 
fairness/bias, and research in this area has mainly focused on detecting bias in a post-hoc 
setting. For example, studies have documented differing performance of existing AA systems 
for test-takers with different gender, race, native language, socioeconomic status, or 
disabilities.  
 
This project will study fairness and ethics in artificial intelligence (AI), with a special focus on 
AA. Studies in machine learning have highlighted that algorithms often introduce their own 
biases either due to an existing bias in the data or due to a minority group being inadequately
informatics-phd-projects-2022-23