Using Machine Learning for University Admission: Mapping the Socio-Technical Issue
O. Niri (TU Delft - Electrical Engineering, Mathematics and Computer Science)
E. Aizenberg – Mentor (TU Delft - Cyber Security)
RL Lagendijk – Graduation committee member (TU Delft - Cyber Security)
H.S. Hung – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Machine learning algorithms were used in in the past decade to assist humans with recruitment and grades assessments in the academic field. For the most part, the algorithms either exacerbated existing biases or output unfair results. This could often be traced back to an ill-implementation of the systems in the social context. The academic admission process is defined as setting goals, locating candidates, ranking and accepting them. To properly integrate machine learning in such process, one may follow the Value Sensitive methodology, which suggests designing a technical system around a social value. This methodology takes into account the various stakeholders, values and technical solutions available. Later, the system should be iteratively improved and constantly evaluated and examined so that it still serves the core values as defined.