Using Machine Learning for University Admission: Mapping the Socio-Technical Issue

Using Machine Learning for University Admission: Mapping the Socio-Technical Issue

Niri, Omri (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Aizenberg, E. (mentor)
Lagendijk, R.L. (graduation committee)
Hung, H.S. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-02

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.

Bias
Machine Learning
Academic admission
Value sensitive deisgn
Socio-technical system deisgn

http://resolver.tudelft.nl/uuid:be135436-2a52-483a-b3bb-cebbe2ed8b6a

Student theses

bachelor thesis

© 2021 Omri Niri