Ethical Governance of AI-Driven Airline Ticket Pricing

Abstract

Artificial intelligence can more and more elevate airlines’ revenue management practices from traditional inventory-based fare setting toward extremely responsive pricing systems that deduce demand, forecast willingness-to-pay, identify booking trends, leverage competition data, and formulate dynamically adjusted offers. While application of such systems can improve revenue prediction and seat utilization, as they become more capable of price differentiation they raise critical ethical concerns including due process, consumer sovereignty, privacy, transparency, explain ability, discrimination, and market competitiveness.

 The line between appropriate dynamic pricing of market fluctuations and inappropriate granular individualized pricing is especially important because travellers might accept a price differential for booking time or seat scarcity but they may not accept an unexplained differential created through profiling. This paper develops and measures an ethical framework for AI-enabled airline ticket pricing using a simulation-based methodology with 1,200 synthetic booking observations for each of four delivery scenarios the prevailing airline revenue management, Markov-chain-based market-dynamic pricing, low-transparency customized AI pricing, and legally constrained customized AI pricing (adding transparency and fairness constraints to preserve the AI decision process).Revenue indices, price dispersion, perceived fairness, perceived transparency, privacy concern, consumer trust, and complaint intention are measured as measurable 

Simulation results show that in the short term, low transparency personalized pricing generates the highest revenue index, and thus maximizes willingness-to-pay, but it also generates the most price dispersion, the greatest privacy concern, the lowest fairness ranking, the lowest consumer trust, and the highest complaint intention. Dynamics of AI application with a constrained and transparent model show much of the revenue advantages of algorithmic pricing while significantly reducing privacy concern and increasing fairness perception and consumer trust. Comparative analysis demonstrates that a point of diminishing return exists and that once instructed which feature tapers should be dropped, AI-driven solutions should incorporate additional governance controls such as transparency and limits on price variation. Regression analysis further reveals that perceived fairness and transparency can significantly enhance consumer trust and perceived price value, while privacy concern can detract from consumer trust and perceived price value.

The research shows that the pursuit of the highest possible willingness to pay is a naive metric alone for AI-enabled airline pricing optimization and provides an agenda for ethical sensitive adoption of such a future technology where data minimization, defensible market segmentation, meaningful price explanations, human accountability, fairness testing, audit trails, consumer disclosure, and governance constraints are integrated into the pricing solution.