4. Electronic Theses and Dissertations (ETDs) - Faculties submissions

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    A computational study of media bias in South African online political news reporting over the period 2021 - 2023
    (University of the Witwatersrand, Johannesburg, 2024) Ngwenya, Nonhlanhla Nomusa; Alence, Rod
    The study examined the presence of tonality bias in South African political news reporting over the period 2021 until mid-2023. The study employed the methods of the Lexicoder Sentiment Dictionary, a lexical-based method, and Latent Semantic Scaling, a semi-supervised machine learning method. Sentiment was utilised as a proxy for tonality. Online commercial media publishers were contrasted against the state-owned news publisher to ascertain how online news reporting contributed to shaping the national agenda, and the framing of political actors and their respective political parties. The Lexicoder Sentiment Dictionary and the Latent Semantic Scaling evidenced that commercial media publishers exhibited positive tonality bias for the Democratic Alliance during the 2021 Municipal Elections. South African media publishers were found to exhibit consistent negative tonality bias when reporting on protest action. The state-owned media publisher was found to drive a pro ruling party sentiment whereas commercial media publishers’ sentiment was anti- populist and agenda-setting. The congruency in political news reporting gave grounds to the call for diversity in publishing
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    Bias in data used to train salesbased decision-making algorithms in a South African retail bank
    (2021) Wong, Alice
    Banks are increasingly using algorithms to drive informed and automated decision-making. Due to algorithms being reliant on training data for the model to learn the correct outcome, banks must ensure that the customer data is securely and fairly used when creating product offerings as there is a risk of perpetuating intentional and unintentional bias. This bias can result from unrepresentative and incomplete training data or inherently biased data due to past social inequalities. This study aimed to understand the potential bias found in the training data used to train sales-based decision-making algorithms used by South African retail banks to create customer product offerings. The research adopted a qualitative approach and was conducted through ten virtual one-on-one interviews with semi-structured questions. Purposive sampling was used to select banking professionals from data science teams in a particular South African retail bank across demographics and levels of seniority. The data collected from the participants in the interviews were then thematically analysed to draw a conclusion based on the findings. Key findings included: An inconsistent understanding across data science teams in a South African retail bank around the prohibition of using the gender variable. This could result in certain developers using proxy variables for gender to inform certain product offerings. A potential gap in terms of the potential usage of proxy variables for disability (due to non-collection of this demographic attribute) to inform certain product offerings. Although disability was not identified as a known biased variable, it did, however, raise the question of whether banks should be collecting the customer’s disability data and doing more in terms of social responsibility to address social inequalities and enable disabled individuals to contribute as effectively as abled individuals. As algorithms tend to generalise based on the majority’s requirements, this would result in a higher error rate of underrepresented groups of individuals or minority groups. This could result in financial exclusion or incorrect products being offered to certain groups of customers iii which, if not corrected, would lead to the continued subordination of certain groups of customers based on demographic attributes.