article · Journal of Engineering Research and Reports
Artificial intelligence systems are increasingly deployed across diverse sectors, raising concerns around transparency, trust, and ethical data handling. Investigating the impact of Explainable AI (XAI) models alongside information governance standards, research using a literature review and a survey of 342 respondents across industries identified key factors in data management. Implementing XAI significantly increases user trust in automated systems when compared to opaque black-box models. Furthermore, XAI adoption correlates strongly with the ethical handling of customer data, demonstrating the value of clear transparency frameworks and governance controls. User education also plays a critical role in building trust and supporting informed decisions regarding interactions with automated systems. To balance technological benefits against potential risks, organisations must integrate explainability techniques, establish structured information governance frameworks, support user education, and foster a broader culture of transparency and responsible data practices.
As organisations rely more on artificial intelligence to process sensitive customer data, opaque algorithms can damage public confidence. Showing that explainable models and governance standards improve trust provides a practical basis for deploying ethical technologies. This helps both businesses and the public ensure that automated systems remain accountable, transparent, and aligned with user expectations.
Organisations adopting artificial intelligence across various industries can apply these insights to design governance frameworks, deploy explainability techniques, and run user training programmes. The study offers a strategic roadmap based on survey data, indicating early-stage organisational guidance rather than a deployable software product. Commercial implementation would require translating these governance and transparency principles into specific technical tools and internal compliance processes.
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The increasing integration of Artificial Intelligence (AI) systems in diverse sectors has raised concerns regarding transparency, trust, and ethical data handling. This study investigates the impact of Explainable AI (XAI) models and robust information governance standards on enhancing trust, transparency, and ethical use of customer data. A mixed-methods approach was employed, combining a comprehensive literature review with a survey of 342 respondents across various industries. The findings reveal that the implementation of XAI significantly increases user trust in AI systems compared to black-box models. Additionally, a strong positive correlation was found between XAI adoption and the ethical use of customer data, highlighting the importance of transparency frameworks and governance mechanisms. Furthermore, the study underscores the critical role of user education in fostering trust and facilitating informed decision-making regarding AI interactions. The results emphasize the need for organizations to prioritize the integration of XAI techniques, establish robust information governance frameworks, invest in user education, and foster a culture of transparency and ethical data use. These recommendations provide a roadmap for organizations to harness the benefits of AI while mitigating potential risks and ensuring responsible and trustworthy AI practices.
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DOI: 10.9734/jerr/2024/v26i71206
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