AI algorithms show promise in academic applicant screening, but ethical and methodological challenges such as transparency and bias mitigation remain.
TML algorithms excelled with structured data; NLP showed value for unstructured data despite challenges (eg, transparency and data needs). Ethical concerns highlight the need for transparency and bias mitigation in AI adoption. Findings emphasize continued research to address ethical and methodological challenges.
Jiang et al. (Wed,) studied this question.