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<p>Artificial intelligence (AI) is increasingly central to socio-economic development, yet public trust and ethical concerns remain critical for its adoption. This study examines stakeholders’ perceptions of AI, the predictors of trust, and associated ethical concerns in the Greater Kumasi Area, Ghana. Using a convergent mixed-methods approach, quantitative data were collected from 287 stakeholders through a survey, complemented by qualitative interviews with nine participants. Quantitative data were analyzed using descriptive statistics and logistic regression, while qualitative data were analyzed thematically. Results show that 39.72% of respondents had a positive perception of AI, with increased efficiency identified as the main benefit. The most prominent concerns were lack of transparency in decision-making and risks to privacy and data security. An overwhelming majority (94.4%) supported the regulation of AI in Ghana. Regression results indicate that occupation significantly predicts trust in AI, with lawyers and university researchers/lecturers showing significantly lower odds of trusting AI compared to AI researchers. The study provides empirical evidence on AI trust in Ghana and demonstrates that professional background plays a significant role in shaping perceptions of emerging technologies.</p>

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Perceptions and predictors of trust in artificial intelligence use: ethical implications and regulatory oversight in Ghana

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Elvis Mwinsome Sobiesuo,
  • Andrew Nketsia Arthur,
  • Dorcas Sekyi,
  • Richard Ofori,
  • Peter Dwumah,
  • Seth Christopher Yaw Appiah

摘要

Artificial intelligence (AI) is increasingly central to socio-economic development, yet public trust and ethical concerns remain critical for its adoption. This study examines stakeholders’ perceptions of AI, the predictors of trust, and associated ethical concerns in the Greater Kumasi Area, Ghana. Using a convergent mixed-methods approach, quantitative data were collected from 287 stakeholders through a survey, complemented by qualitative interviews with nine participants. Quantitative data were analyzed using descriptive statistics and logistic regression, while qualitative data were analyzed thematically. Results show that 39.72% of respondents had a positive perception of AI, with increased efficiency identified as the main benefit. The most prominent concerns were lack of transparency in decision-making and risks to privacy and data security. An overwhelming majority (94.4%) supported the regulation of AI in Ghana. Regression results indicate that occupation significantly predicts trust in AI, with lawyers and university researchers/lecturers showing significantly lower odds of trusting AI compared to AI researchers. The study provides empirical evidence on AI trust in Ghana and demonstrates that professional background plays a significant role in shaping perceptions of emerging technologies.