Author

  • Faten Elina Kamaruddin

    • Faculty of Computing, Informatics and Mathematics, UiTM Johor Branch, Malaysia
    • Malaysia

  • Nur Hanisah Mohamad Razali

    • Faculty of Business and Management, UiTM Johor Branch, Malaysia
    • Malaysia

  • Ahmad Fuzi Md Ajis

    • Faculty of Computing, Informatics and Mathematics, UiTM Johor Branch, Malaysia
    • Malaysia

  • Nur Rifhan A Rahim

    • Faculty of Business and Management, UiTM Johor Branch, Malaysia
    • Malaysia

  • Siti Noorhaslina Abd Halim

    • Faculty of Business and Management, UiTM Johor Branch, Malaysia
    • Malaysia

  • Ainol Mardhiyah Rahmat

    • Faculty of Business and Management, UiTM Johor Branch, Malaysia
    • Malaysia

Abstract

This study proposes a conceptual framework to investigate factors influencing the data partiality in Artificial Intelligence (AI). The popularity of AI is world-renowned machines that can represent human intelligence under the program of computer systems. However, the academic research on data partiality focusing on AI is limited across the bibliographic database sources. This study aims to address the gaps by proposing a developed framework that integrates three factors: the AI algorithm, black data, and user revise terminology highlighted in the past literature. The AI algorithm refers to the issues on the training data as a dataset used in the tools, which stimulates the data partiality as the outcome retrieved by the user. The black data is influencing data partiality on the existence of unknown data. The user revise terminology represented on the keywords used by the user to search for information, which incorrect keywords with not specify will lead to the AI to give all related information as an output without filter. The framework asserts that these three elements directly affect the partiality of data in AI. A quantitative methodology will be used in this study to cover the collection of survey data from the community under the MDEC program called Global Online Workforce (GLOW). The framework contributes a theoretical understanding of A I algorithms, black data, and user-revised terminology that influence data partiality in AI. In future research, the framework can be extended to test the data partiality in AI tools used in information agencies, as these bodies govern the safeguards of the accuracy of the information.

Keywords

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How to Cite

Kamaruddin, F. E., Mohamad Razali, N. H., Md Ajis, A. F., A Rahim, N. R., Abd Halim, S. N., & Rahmat, A. M. (2024). Factors Influencing Data Partiality in Artificial Intelligence. Information Management and Business Review, 16(3S(I)a), 1161-1169. https://doi.org/10.22610/imbr.v16i3S(I)a.3861

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Copyright (c) 2024 Faten Elina Kamaruddin, Nur Hanisah Mohamad Razali, Ahmad Fuzi Md Ajis, Nur Rifhan A Rahim, Siti Noorhaslina Abd Halim, Ainol Mardhiyah Rahmat (Author)

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Vol. 16 No. 3S(I)a (2024) Supply Chain Resilience, Islamic Finance & Micro-Influence View issue contents

Published

2024-10-27

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