Artificial intelligence-driven decision-making and its impact on board accountability
TL;DR
It was observed that AI-driven decision-making helps minimize bias if organizations use special AI algorithms for analyzing large amounts of data and producing conclusions from them.
Abstract
This study explored the impact of artificial intelligence (AI)-driven decision-making (data-backed insights, risk management, efficiency and speed, predictive analytics, bias mitigation, continuous learning, and auditability) on board accountability within commercial banks in Jordan. It utilized the expanding body of literature on AI governance and corporate accountability (Wirtz et al., 2020; Dwivedi et al., 2023). To achieve this aim, a quantitative methodology was employed, using a self-administered questionnaire completed by 72 individuals from 16 commercial banks in Jordan. Primary data were analyzed using the SPSS software, and it was determined that AI-driven decision-making has the ability to positively influence board accountability within commercial banks and the banking sector in general. Among the adopted sub-variables, it was observed that AI-driven decision-making helps minimize bias if organizations use special AI algorithms for analyzing large amounts of data and producing conclusions from them. By adopting AI-driven decision-making, organizations will be in a better position to enhance their governance system, improve transparency, and improve the overall perception of stakeholders in the banking industry.