Reliable and fair machine learning for risk assessment

Pereira Barata, A.P. (2023)

Doctoral Thesis

The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.
The goal is develop and assess the technical methods which are required to shift the actions of the Inspectorate to a data-driven paradigm, concretely under a supervised classification framework of machine learning.
The aspect of reliability is addressed as a data quality concern, viz. missingness and noise.
The aspect of fairness is addressed as a counter to bias in the selection process of inspections.
The conclusion is that, whilst no complete solution has yet been suggested, it is possible to address the concerns related to data quality and data bias, culminating in well-performing classification models which are reliable and fair.

• Trustworthy AI
• Machine learning
• Classification
• Supervised
• Fairness
• Noise
• Missingness
• Inspection
• Data quality
• Bias

• All authors: Pereira Barata, A.P.
• Supervisor: Herik, H.J. van den
• Co-supervisor: Veenman, C.J.; Takes, F.W.
• Committee: Bäck, T.H.W.; Jonker, C.M.; Dignum, M.V.; Kok, J.N.; Hoos, H.H.
• Qualification: Doctor (dr.)
• Awarding Institution: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
• Date: 2023-04-05
• Title of host publication: SIKS Dissertation Series
• ISBN (print): 9789464197198
• Name: 2023-06