AI researcher Anmol Aggarwal explains how fairness-aware pricing algorithms can reduce hidden bias without major revenue loss.
AI pricing fairness
From ride-hailing fares and online shopping carts to subscription plans and travel bookings, algorithms now play a central role in determining what consumers pay. While AI-driven pricing systems promise greater efficiency and personalization, they have also raised concerns about fairness, transparency, and long-term trust. Following the publication of two recent IEEE conference papers, AI systems researcher Anmol Aggarwal discusses how pricing algorithms can produce hidden disparities and how fairness can be deliberately incorporated into large-scale optimization systems.
Q: You recently published two IEEE conference papers, “Fairness-Aware Personalized Pricing: A Simulation Study of Trade-offs Across Behavioral, Group, and Envy-Based Constraints” and “GTSO++: Fairness-Aware Causal Uplift for Tiered Pricing with Generative Personas.” What problem do these papers address, and why does it matter now?
Anmol Aggarwal: Personalized pricing and promotions are now driven by machine learning across many digital products. The core issue is that systems optimized purely for short-term revenue can create hidden unfairness. The first paper examines fairness trade-offs in personalized pricing across multiple fairness constraints. The second paper, GTSO++, introduces a fairness-aware causal uplift approach for tiered pricing and uses generative personas to evaluate outcomes across diverse user behaviors. The research is particularly relevant as pricing systems increasingly operate at scale, and without explicit fairness objectives, it becomes challenging to audit or correct their long-term impact.
Q: In simple terms, what makes AI-driven pricing “unfair,” and what does your first paper add to existing discussions about price discrimination?
Aggarwal: Unfairness in AI pricing is often not about a single price difference. It emerges as a pattern over time. What the paper adds is a structured way to compare different fairness definitions that are often discussed separately. Instead of treating fairness as one vague concept, we measure behavioral, group, and envy-based fairness side by side in a controlled simulation. This allows us to quantify the trade-offs involved and understand how different fairness goals change system behavior.
Q: You focus on three fairness types. Can you briefly explain what they mean in the context of pricing?
Aggarwal: Behavioral fairness looks at whether specific behavior segments are systematically disadvantaged. A typical example is loyal or high-intent users consistently paying more because models assume they will convert regardless.
Group fairness focuses on parity across defined groups, where the concern is whether pricing outcomes align with demographic or categorical differences.
Envy-based fairness comes from economics and asks whether one individual would prefer another person’s outcome. In pricing terms, it relates to how often users experience worse prices than comparable users, which directly affects perceived fairness and trust.
Q: Your study emphasizes trade-offs. What happens when companies enforce fairness constraints in pricing systems?
Aggarwal: The primary trade-off is between unconstrained revenue optimization and limiting disparities. Stronger fairness constraints reduce the degrees of freedom available to the algorithm, which can lower short-term uplift. However, our results show that the “price of fairness” is often modest. In many scenarios, meaningful reductions in disparity come with relatively small performance losses.
One finding highlighted by the research is a design decision with measurable consequences. Systems should make these trade-offs explicitly, rather than allowing unfair patterns to emerge unintentionally.
Q: Your second paper uses causal uplift modeling. Why is this approach particularly suited to pricing and promotions?
Aggarwal: Standard prediction models estimate whether someone will buy, but they do not answer whether an intervention changes the outcome. Causal uplift modeling focuses on the incremental effect of an offer compared with no offer. This distinction matters because giving discounts to users who would buy anyway can be inefficient and can also create perceived unfairness.
Q: What does GTSO++ add beyond traditional uplift-based systems?
Aggarwal: GTSO++ integrates fairness constraints directly into the uplift-based allocation of pricing tiers or offers. Traditional systems rank users purely by expected incremental response. GTSO++ ensures that the final allocation also respects fairness objectives, rather than optimizing uplift alone.
The method also introduces generative personas as a stress-testing layer. Persona-based simulation helps surface fairness failures that might otherwise remain hidden until after deployment.
Q: Generative personas may sound abstract. How are they used in practice in your research?
Aggarwal: In this work, generative personas are simulated user profiles with controlled behavioral traits such as loyalty, impulsivity, price sensitivity, or trust decay. The goal is not to replace real data, but to probe system behavior under varied conditions.
For example, we can simulate how a pricing policy behaves if a segment becomes more price-sensitive, or if repeated perceived unfairness erodes trust. This allows teams to test robustness and fairness before policies reach production.
Q: What should companies take away from this research if they already rely on A/B testing and revenue metrics?
Aggarwal: A/B tests and revenue metrics are necessary, but they are not sufficient. Companies should track disparity metrics aligned with their fairness goals, and distinguish correlation from causal impact when allocating offers.
Pre-deployment stress-testing, including persona-based simulation, can help identify risks earlier in the decision pipeline.
Q: What keeps you grounded outside of work?
Aggarwal: Continuous learning and mentoring help maintain perspective.
The shift toward pervasive AI-driven pricing makes responsible optimization a central challenge. This research highlights how fairness must be addressed deliberately in system design. Aggarwal contends that future pricing systems will be evaluated not just on performance, but also on the transparency and equity with which they treat users.
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