As AI Decides Air Ticket Prices, Fairness Remains a Question
When you refresh a flight search and see the fare jump within minutes, chances are it’s not a coincidence, but AI at work. Airlines around the world are increasingly relying on AI-powered systems to forecast demand, set fares and micro-target passengers.
On a positive note, technology is bringing efficiency and fuller flights. At the same time, it is also raising concerns about the fairness and privacy of consumers.
A month ago, United States transportation secretary Sean Duffy reportedly said there are concerns about the use of artificial intelligence to set personalised airline prices, echoing red flags brought up by three Democratic senators.
“Airlines now feed machine-learning systems with far more than just historical bookings,” said Jaspreet Bindra, co-founder and CEO of AI & Beyond. These models ingest booking velocity, cancellations, competitor fares, macro-economic indicators, weather forecasts, major events, and even online chatter.
“The algorithms detect non-linear patterns and adapt continuously, spotting demand shifts much faster than seasonal, rule-based methods,” he added.
The payoff, Bindra explained, is materially better forecasts and fewer surprise swings in load factors. According to industry reports, 85% of airlines plan to increase AI investment in 2025 to strengthen demand and revenue management.
Dr Anshu Jalora, founder & MD of Sciative Solutions, broke down the mechanics further: AI-powered airline pricing works by continuously analysing massive volumes of data, including seat availability, historical booking patterns, competitor fares, time to departure, market demand and even special events.
“At Sciative, we combine proprietary deep learning forecasting models with mathematical optimisation and strict guardrails. Reinforcement learning algorithms then refine pricing every 15 minutes, creating a self-learning, closed-loop system.”
Real-time Pricing and Micro-Segmentation
Unlike older systems that updated fares in fixed cycles, modern AI engines now work as “living feedback loops.”
“They digest live seat inventory, booking lead times, load factors, competitor pricing, cancellation risk, and even spikes in online searches,” Bindra said. The system scores price elasticity for each micro-segment and nudges fares up or down, sometimes dozens of times a day.
This micro-segmentation also explains why two passengers might see different fares for the same seat. “Not because the seat changed,” Bindra clarified, “but because the algorithm inferred different purchase propensities. About 65% of passengers say they prefer airlines that personalise services, but that same personalisation also drives fare variations.”
Dr Jalora identified the three most influential factors in these AI-led price shifts: time of departure, real-time demand and inventory levels, and competitive price positioning. “Early in the booking window, the system may set lower fares to stimulate demand. Closer to departure, it may raise prices if demand is strong, or cut them if seats remain unsold,” he said.
Balancing Revenue with Fairness
The biggest consumer concern is fairness, whether AI is simply helping airlines squeeze more money from passengers.
Bindra admitted that during low-demand periods, AI often reduces prices to fill seats, improving affordability. During peak windows, such as festivals or major sports events, algorithms tend to price more aggressively than static systems.
“The net effect is fuller flights and better market efficiency, but also sharper price swings.”
Dr Jalora emphasised that fairness needs to be built into the design. “We define ethical constraints directly into the pricing engine, caps on surge multipliers, seasonal limits, and anti-gouging rules during emergencies,” he said.
“Instead of personal profiling, we use predictive segmentation models that adjust fares based on context, not identity. Every price decision is logged with audit trails to ensure transparency.”
India’s Adoption Curve
Globally, AI-led pricing is mainstream. In India, adoption is accelerating but unevenly. “Air India has committed $200 million to its Vihaan.AI digital transformation, with pricing expected to benefit significantly,” Jalora noted.
“IndiGo is pushing AI on customer service through its chatbot 6Eskai, but hasn’t confirmed full-scale dynamic pricing yet. Akasa Air uses AI for operations and route planning, though again, full fare optimisation isn’t public. I’d say widespread adoption in India is still two to three years away,” he said.
All major Indian airlines, meanwhile, declined to comment on the adoption of AI in their pricing strategy.
“The main risks are opaque personalisation, proxy discrimination, and the loss of consumer trust,” Bindra warned. Airlines need guardrails to ensure pricing isn’t indirectly biased. Without transparency, dynamic pricing can feel arbitrary or even predatory.
Dr Jalora outlined what must be mandatory: an explainability layer so every fare can be traced back to demand or inventory triggers, regular bias detection audits, embedded compliance constraints, and continuous revalidation of guardrails.
“AI-led pricing should not only be profitable, but also defensible, explainable, and fair.”
Looking ahead, experts expect hyper-contextualisation: real-time bundles, loyalty-driven deals, and subscription-based flying models. The focus will be on creating stable, principled pricing that balances profitability with customer trust.
The post As AI Decides Air Ticket Prices, Fairness Remains a Question appeared first on Analytics India Magazine.
