Ethics of AI-based supply chain optimization: a better balance between efficiency and fairness

This study examines the ethical issues related to AI-driven supply chain optimization, such as algorithmic biases, the effects of automation on employment, and accountability and transparency. Given the goal of increasing efficiency, machine learning, predictive analytics, natural language processing, and artificial intelligence (AI) are being actively used in a variety of industries, including retail, healthcare, and logistics. Also, technologies are automating and improving tasks such as inventory tracking and demand forecasting. This lowers cost and increases supply chain flexibility. However, using them raises significant ethical problems, specifically the issue of making fair choices. With the presence of bias in the trained systems, there will be unfair distribution of resources and the conditions that define the consequences of decisions, such as the introduction of high-value goods over fundamental needs, and in this case, the population needs it most. Another important issue is job loss, especially in low-skilled jobs, as automation becomes the norm in the logistics industry. The study suggests that AI systems should adopt ethical principles, such as fairness, transparency, and accountability. It suggests practical steps that businesses should take to employ AI in ways that ensure everyone gets fair results. The study continues by emphasizing the need to be aware of ethical issues to use AI to improve efficiency while also promoting fairness and sustainability in global supply chain management.