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Detecting Online Price Discrimination

Started ‎03-17-2021 by
Modified ‎10-20-2022 by
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Detecting Online Price Discrimination
Team Name FairPricing
Track Retail
Use Case Detecting and Mitigating Bias in Online Pricing
Technology SAS Viya, Machine Learning
Region USA
Team lead Dr Jose Mendoza
Team members Dr Diala Ezzeddine
Pricing is undoubtedly one of the most important variables of the marketing mix. As such, pricing impacts profits but also signal quality, and is intrinsically related to perceptions of fairness. Pricing and Fairness have been present for so long, with evidence dating back to the Middle Ages, and the Just Price doctrine of St. Thomas Aquina. 
However, it is now, perhaps more than ever, that the issue of fair prices reaches a new dimension. On the one hand, the availability of information, that is spread over the Internet, and more specifically, over Social Media, is allowing customers to be more aware of pricing issues, such as those resulting from unfair prices, and price discrimination. On the other hand, the current pandemic is accelerating online purchasing, reaching greater demographic groups.
For example, groups that are identified as "protected groups", based on demographic variables such as age, race, and income, are rapidly moving online. These protected groups have traditionally been suffering from bias and fairness issues. As an example, there is the notion of Food Deserts in the USA. Accordingly, Food Deserts are areas with limited access to healthy food due to poor access to supermarkets and grocery stores. Food Deserts tend to be disproportionally in rural and low-income areas. 
Moreover, this idea of unfair access is not limited to food. For example, research has shown that older age groups, as well as low-income groups, might not have access to offers, discounts, and low rates in loans and credit cards, based on factors such as location, for example.
Notwithstanding, the issue here is that there is a risk that these "deviations" and "vices" might transfer to the online world, and continue hammering vulnerable groups. There are good reasons for these concerns, for example, research has shown that price discrimination and steering does exist in online retail (Hindermann, 2018), and here are some documented cases:
  • Asians were twice as likely as non-Asians to have higher online prices for SAT tutoring (Angwin et al, 2015)
  • Capital One offered different loan rates depending on the browser used (Devin, 2010)
  • Orbitz offered Apple steered users to more expensive hotels than Windows users (Vissers et al, 2014)
with more cases reported rapidly, including anecdotal cases, such as in the case of the author of this article, that found higher prices from when shopping from inside the Affirm app, compared than shopping at the same time, for the same flight, in the same device, from the Safari browser.
So, there are many risks associated with online price discrimination, including consumer backlash, regulatory problems, legal problems, and negative word of mouth (i.e., such as the one resulting from Social Media).
Therefore, there is an increasing need for marketers, and in particular, retailers, to ensure that its online pricing practices are fair, and are not hurting consumers. Our work will highlight different approaches, some of those who have been reported in research:
  • Self-reporting
  • Crowdsourcing
  • Smart agents
  • Simulations

Technology is bringing the possibility for bringing radical transformations in our daily life. Let's use technology for good, let's use data for good.

We really enjoyed participating in the SAS Hackathon 2021. Please, look at our short and long videos below.


FairPricing Team


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Last update:
‎10-20-2022 12:19 PM
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