Ethical Considerations in AI-driven Dynamic Pricing in the USA: Balancing Profit Maximization with Consumer Fairness and Transparency

Abstract

Organizations in the USA are progressively employing AI-driven dynamic pricing as a strategic intervention to flexibly modify their prices based on competition, market demand, and various other factors. This research paper focused on the ethical dimensions of AI-driven dynamic pricing and the crucial interplay between profitability and the establishment of unwavering consumer transparency and fairness. The recommended models for dynamic pricing solutions entailed ensemble learning methods, notably, XG-Boost, Light-GBM, Cat-Boost, and X-NGBoost models. Particularly, the proposed model consolidated the XG-Boost algorithm and the NG-Boost model, resulting in a novel methodology termed the X-NGBoost. To compare and contrast the performance of the proposed models, these algorithms were trained and subjected to the same dataset. The comparison between the models was mainly grounded on the root-mean-square error (RMSE) metric, which was quantified in meters. The results indicated that X-NGBoost had the lowest RMSE on both the testing and training sets, at 4.23 and 5.34 respectively. This indicated that X-NGBoost performed very well on both seen and unseen data. Therefore, from the outcomes it was deduced that, for the provided data set, the X-NGBoost model provided the accurate pricing solution.