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A study of fairness in machine learning in the presence of missing values
Thesis (MCom)--Stellenbosch University, 2023.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2023
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| _version_ | 1867613767866515456 |
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| access_status_str | Open Access |
| author | Bhatti, Aeysha Aziz |
| author2 | Sandrock, Trudy |
| author_browse | Bhatti, Aeysha Aziz Sandrock, Trudy |
| author_facet | Sandrock, Trudy Bhatti, Aeysha Aziz |
| author_sort | Bhatti, Aeysha Aziz |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/128499 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:41:23.238Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/128499 A study of fairness in machine learning in the presence of missing values Bhatti, Aeysha Aziz Sandrock, Trudy Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science. Machine learning Big data Machine learning -- Mathematical models Computer algorithms UCTD Thesis (MCom)--Stellenbosch University, 2023. ENGLISH SUMMARY: Fairness of Machine Learning algorithms is a topic that is receiving increasing attention, as more and more algorithms permeate the day to day aspects of our lives. One way in which bias can manifest in a data source is through missing values. If data are missing, these data are often assumed to be missing completely randomly, but usually this is not the case. In reality, the propensity of data being missing is often tied to socio-economic status or demographic characteristics of individuals. There is very limited research into how missing values and missing value handling methods can impact the fairness of an algorithm. In this research, we conduct a systematic study starting from the foundational questions of how the data are missing, how the missing data are dealt with and how this impacts fairness, based on the outcome of a few different types of machine learning algorithms. Most researchers, when dealing with missing data, either apply listwise deletion or tend to use the simpler methods of imputation versus the more complex ones. We study the impact of these simpler methods on the fairness of algorithms. Our results show that the missing data mechanism and missing data handling procedure can impact the fairness of an algorithm, and that under certain conditions the simpler imputation methods can sometimes be beneficial in decreasing discrimination. AFRIKAANSE OPSOMMING: Die regverdigheid van masjienleeralgoritmes is ’n onderwerp wat toenemend aandag geniet, soos al hoe meer algoritmes elke aspek van ons alledaagse lewens deurdring. Een manier waarop sydigheid in ’n databron kan manifesteer is deur ontbrekende waardes. Indien daar ontbrekende data is, word daar dikwels aanvaar dat die data op ’n algeheel ewekansige manier ontbrekend is, maar dit is gewoonlik nie die geval nie. In werklikheid is die geneigdheid vir die afwesigheid van data dikwels eerder as meer komplekse metodes wanneer hulle met ontbrekende waardes gekonfronteer word. Ons ondersoek die impak van hierdie eenvoudiger metodes op die regverdigheid van algoritmes. Ons resultate toon dat die onderliggende ontbrekende waarde meganisme en die prosedure vir die hantering van ontbrekende waardes die regverdigheid van ’n algoritme kan beinvloed, en dat onder sekere kondisies die eenvoudiger imputasiemetodes soms kan help om diskriminasie te verminder. Masters 2023-03-01T08:54:25Z 2023-08-30T13:07:36Z 2023-03-01T08:54:25Z 2023-08-31T09:18:53Z 2023-03-01T08:54:25Z 2023-08-31T09:18:53Z 2023-03 Thesis https://scholar.sun.ac.za/handle/10019.1/128499 en_ZA Stellenbosch University application/pdf xi, 125 pages : illustrations, includes annexures application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Machine learning Big data Machine learning -- Mathematical models Computer algorithms UCTD Bhatti, Aeysha Aziz A study of fairness in machine learning in the presence of missing values |
| title | A study of fairness in machine learning in the presence of missing values |
| title_full | A study of fairness in machine learning in the presence of missing values |
| title_fullStr | A study of fairness in machine learning in the presence of missing values |
| title_full_unstemmed | A study of fairness in machine learning in the presence of missing values |
| title_short | A study of fairness in machine learning in the presence of missing values |
| title_sort | study of fairness in machine learning in the presence of missing values |
| topic | Machine learning Big data Machine learning -- Mathematical models Computer algorithms UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/128499 |
| work_keys_str_mv | AT bhattiaeyshaaziz astudyoffairnessinmachinelearninginthepresenceofmissingvalues AT bhattiaeyshaaziz studyoffairnessinmachinelearninginthepresenceofmissingvalues |