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Rethink fairness in the age of A.I.
Rethink fairness in the age of A.I.
Markets have long been shaped by power imbalances between large corporations and their smaller counterparts, as well as between companies and consumers. In the age of AI-driven commerce, these disparities have only intensified. Dominant firms increasingly leverage vast volume of user and sales data to fine-tune product recommendations, dynamically set prices, and optimize for maximum revenue. Their access to advanced algorithms and computational resources gives them a structural advantage that smaller businesses simply cannot match.
This technological edge raises serious concerns about fairness. Consumers may face opaque pricing practices and limited visibility into alternative options, while small and new businesses struggle to gain traction in algorithmically curated marketplaces. Despite the growing complexity of these dynamics, academic research often focuses primarily on firm-side optimization—revenue, engagement, or conversion—without sufficient attention to consumer welfare or equitable competition.
My research on Market Fairness has a simple but critical mission: to investigate and promote fairness in modern digital marketplaces. The objective is to empower small businesses with tools, insights, and policy frameworks that level the playing field, and to advocate for consumer protections in pricing, recommendation, and ranking systems. By integrating perspectives from operations management, behavioral economics, marketing, and computer science, I am inspired to promote that innovation in AI benefits not just the powerful, but everyone.
Behavioral Aspects and Fairness
Behavioral Aspects and Fairness
Being human means we all have our limitations. But our inherent biases could be easily exploited by companies for their bottom lines.
Time Discounting
Time Discounting
Consumers tend to place greater weight on immediate costs and benefits than on those occurring in the future. As a result, payment plans that lower upfront costs, such as "buy now pay later" or installment options, can appear more attractive than paying in full, even when they carry high long-term interest or hidden fees. When platforms or lenders fail to clearly disclose interest rates or the true total cost alongside these options, consumers may be misled into making suboptimal financial decisions. This lack of transparency creates an unfair decision-making environment, disproportionately affecting those with lower financial literacy and undermining fair consumer choice.
Rational Inattention
Rational Inattention
Due to cognitive limitations, consumers tend to focus their attention on information they perceive as most relevant or trustworthy. On e-commerce platforms, where thousands of products compete for attention, users often rely heavily on ranking systems to guide their purchasing decisions. However, these rankings are typically optimized to maximize platform revenue, not necessarily to reflect product quality, value, or consumer utility. Most consumers are unaware of the commercial logic behind these algorithms and may mistakenly assume that higher-ranked items are objectively better or more relevant. Moreover, ranking systems frequently incorporate popularity metrics—such as past sales or click-through rates—which can systematically disadvantage small or new businesses that have not yet accumulated visibility or traction.
"The Google search algorithm cannot taste ice cream... and the most successful ice cream vendors in the world are those that the Google Algorithm ranks first." Yuval Noah Harari
"The Google search algorithm cannot taste ice cream... and the most successful ice cream vendors in the world are those that the Google Algorithm ranks first." Yuval Noah Harari
Risk Averse
Risk Averse
Human decision-making is often influenced by cognitive biases rather than statistical reasoning. For example, many people prefer a guaranteed gain of $50 over a 50% chance of gaining $110, yet would choose a 50% chance of losing $110 over a certain loss of $50. One real-world implication of this bias is seen in cancellation-related pricing strategies in industries like airlines and hospitality. Companies offer different price tiers based on cancellation flexibility, yet provide little transparency about how these premiums are calculated. Consumers must choose between options despite varying degrees of uncertainty in their travel plans, often without a clear understanding of the financial trade-offs. This can lead to decisions that do not align with their best interests, especially when their risk preferences or travel uncertainties are not adequately accommodated.
Other Research Topics
Other Research Topics
Price Discrimination
Price Discrimination
What constitutes price discrimination when firms personalize prices based on consumer data (e.g., location, browsing behavior, device type)?
What tools, signals, or patterns can consumers use to identify if they are being subjected to discriminatory pricing?
How can pricing algorithms optimize company revenue without compromising consumer welfare?
Platform Power and Seller Constraints
Platform Power and Seller Constraints
What are the dominant forms of contractual or algorithmic leverage that large digital platforms use to constrain third-party sellers (e.g., ranking bias, exclusivity clauses, high commission fees)?
What strategies can sellers employ to resist or negotiate fairer terms with powerful platforms?
What is the long-term effect of platform-imposed restrictions on the sustainability and growth of small or independent sellers?
Visibility of Small and New Businesses in Digital Markets
Visibility of Small and New Businesses in Digital Markets
How do consumers perceive and engage with small or new businesses when they appear in search results, particularly in comparison to established brands?
Can algorithmic interventions in ranking systems (e.g., fairness-aware ranking, randomization, diversity-based rankings) improve discoverability of small or new businesses without degrading user experience?
How can ranking systems balance relevance with exposure, especially when default ranking methods may reinforce biases toward known brands?
Can we quantify the diversity of search results and use that as a benchmark for fair exposure?
AI for fairness
AI for fairness
How can AI help consumers to optimize their utilities? For example, purchasing decisions based on preferences, budgets, and timing?
Can AI tools be made transparent enough for consumers to trust their recommendations without feeling manipulated?
How can small business utilize AI with limited resources to optimize their revenue in competition with large corporations?
Can small businesses collaborate (e.g., via cooperatives or data-sharing networks) to pool data and benefit from collective AI models?
Interested in these topics? Feel free to email me at jj438@cornell.edu for further discussion.
Cases/Media Reports
Cases/Media Reports
Higher fares for solo travelers
Source: US Airlines Are Quietly Hitting Solo & Biz Travelers with Higher Fares. Kyle Potter @ Thrifty Traveler.
What's being reported: "All three of the country's largest carriers (American Airlines, United Airlines, and Delta) are penalizing solo travelers with higher ticket prices than you can book when traveling with a group – sometimes, significantly higher."
Why it hurts consumers: This is a pretty typical example of price discrimination. (Definition of price discrimination according to Cornell Law School's Legal Information Institute: Price discrimination refers to charging different customers different prices for the same good or service.) In this case, solo travelers are not receiving different services compared to other types of travelers, nor do they have any impact on the services experienced by others. Hence, higher fares based solely on their travel status are not justified.
Opinion from OM perspective: Solo travelers should be compensated instead! Remember the single rider line at Disney? Solo travelers can serve a similar purpose here—filling out single seats, usually the middle seat that nobody wants. Clearly, airlines are able to charge solo and business travelers more because they tend to be less price sensitive compared to, say, a family of six. But this should not be a signal that they should be exploited by airline revenue managers.
What can be done instead: To make the operations smoothly, airlines could assign solo travelers to a later boarding group. Unlink group travelers, they may have fewer luggages and have no need to take care of accompanied travelers (e.g., children or seniors).
Recommended Readings
Recommended Readings
Papers
Papers
Son, Y., Wowak, K. D., & Angst, C. M. (2024). Does Greater Visibility Benefit Minority Businesses? Evidence from an Online Review Platform. Production and Operations Management, 10591478241243381. https://doi.org/10.1177/10591478241243381
Calvano, E., Calzolari, G., Denicolò, V., Harrington, J. E., & Pastorello, S. (2020). Protecting consumers from collusive prices due to AI. Science, 370(6520), 1040–1042. https://doi.org/10.1126/science.abe3796
Hunold, M., Kesler, R., & Laitenberger, U. (2020). Rankings of Online Travel Agents, Channel Pricing, and Consumer Protection. Marketing Science, 39(1), 92–116. https://doi.org/10.1287/mksc.2019.1167
Yang, Z., Fu, X., Gao, P., & Chen, Y.-J. (2024). Fairness Regulation of Prices in Competitive Markets. Manufacturing & Service Operations Management, 26(5), 1897–1917. https://doi.org/10.1287/msom.2022.0552
Cohen, M. C., Elmachtoub, A. N., & Lei, X. (2022). Price Discrimination with Fairness Constraints. Management Science, 68(12), 8536–8552. https://doi.org/10.1287/mnsc.2022.4317
Cohen, M. C., Miao, S., & Wang, Y. (2025). Dynamic Pricing with Fairness Constraints. Operations Research. https://doi.org/10.1287/opre.2023.0123
Malc, D., Mumel, D., & Pisnik, A. (2016). Exploring price fairness perceptions and their influence on consumer behavior. Journal of Business Research, 69(9), 3693–3697. https://doi.org/10.1016/j.jbusres.2016.03.031
Book
Book
Hoffman, A. J. (2025). Business School and the noble purpose of the market: Correcting the systemic failures of shareholder capitalism. Stanford Business Books, an imprint of Stanford University Press.
Report
Report
BEUC. (2024). Digital Fairness for Consumers. https://www.beuc.eu/reports/digital-fairness-consumers
Google Sites
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