The study assesses the application of AI and ML in decision-making across four sectors: healthcare, banking, manufacturing, and retail. To measure the utilization in actual situations, the form of a mixed method unites expert interviews and questionnaires with simulated case studies. AI and ML serve to improve the accuracy of predictions, automate routine tasks, and customize processes. But there are issues with providing assurance information is unbiased and with model transparency and ethics concerns. Towards this end, the study advocates for implementing industry-specific regulations and human-machine collaboration in realizing optimal benefits optimally in a fair manner while limiting associated risks. Thus, the analysis’ top priority is still in adopting responsible and ethical approaches under each sector’s framework. For this research, the outcome emphasizes the ways in which AI and ML continuously increase predictive precision, enable predictive maintenance, enable drudgery to be made rational in routine, and drive customer experience customization. That is, there remain some challenges: data bias, model building ambiguity, actual or perceived ethical issues confusing AI, or assumed over-reliance on computer software designed to shatter some form of human oversight. The present study presents new avenues of operational transparency, data integrity, and human-machine interface to facilitate co-working for best utilization of fullest potential of such technologies. It also posits that these strategies must be custom and agile to each sector based on its technology maturity level, regulatory conditions, or situations. In a way, this study validates profit-generating applications of AI-ML decision-making and yet expresses its concern for ethical, responsible, and inclusive ways of doing so that ultimately push some of the risks to human well-being and accumulate the behemoth potential of humankind.
Ayesha et al. (Wed,) studied this question.