Persistent URL of this record https://hdl.handle.net/1887/3590289
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- Title Pages_Contents_Glossary
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- Chapter 2
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- Chapter 3
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- References
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- Summary in English
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- Summary in Dutch
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- Propositions
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Reliable and fair machine learning for risk assessment
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.
- 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
Publication Series
- Name
- 2023-06