Dissertation
Reliable and Fair Machine Learning for Risk Assessment
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.
- Author
- Pereira Barata, A.P.
- Date
- 05 April 2023
- Links
- Thesis in Leiden Repository
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.