With multiple billions of packages shipped per year, and even more billions of items purchased, it’s no wonder that Amazon is a household name. In the time it takes you to read this article, an estimated 100,000 items will be purchased on Amazon. But how many of those purchases will be impacted by data bias? It’s likely every single one.
Have you ever stopped to think about the algorithms behind those convenient home page suggestions, people also purchased, and related to items you viewed? Maybe in passing or idly but likely not in detail. In this post we will be exploring just how much one quick search on Amazon can be littered with bias.
Bias can end up in your shopping cart in many different stages along the way. It’s safe to assume that Amazon wants to maximize it’s bottom line, even though they claim they want to provide the consumer with a great deal. When the engineers set to writing shopping algorithms at Amazon corporate, they have to add parameters that are computable and achieve a certain goal. With this in mind, they are likely adding the bias that they would like to increase Amazon’s bottom line (aka, profitability of your purchase). Let’s apply this to an example search.
Perhaps you’ve been searching for storage solutions for your messy closet. The moment your results appear for “closet storage” you are encountering data bias. Exhibit A, the screenshot below:
You’re looking at the first instance of bias because a majority of the page is showing you “sponsored” results. “Funding Bias” is written into many algorithms all over the internet and is the reason why sponsored items always show up first. These brands: JYYG, TYPE A, OUMYJIA and JEROAL are paying to bias your search. This is one of, if not the most common search bias encountered but we see so much “sponsored” content that, at this point, we may not even think of the bias’s effects.
Regulations have been placed on funding bias, note the “Sponsored” tag and other evidence for example on social media platforms when influencers post with “#ad.” These indicators are set up to clue in the consumer that their purchasing decisions are being biased by funded posts, something relatively new the last several years. Corporations spent years biasing search history towards paid posts without having to let the consumer know, this is a huge stride against hidden data bias. There’s nothing wrong with “pay to play!” As a consumer, this can be a helpful way to find one of your new favorite brands, products or technology solutions but people now believe it’s important for the consumer to know when funding is biasing their purchase.
Another example of bias in this search are the items marked with the “best seller” tag. Under “Help & Customer Service” Amazon.com says: “The Amazon Best Sellers calculation is based on Amazon.com sales and is updated hourly to reflect recent and historical sales of every item sold on Amazon.com.” There is a litany of data bias that could be going on here. Was the “Simple Houseware” organizer at one point a “sponsored” post which led to it becoming a “best seller?” A quick google search shows that Simple Houseware only sells on Amazon, this could be another contributing factor to the status as a “best seller.” We simply do not know the parameters the calculation is setting for “sales” as that is quite a broad term.
To play devil’s advocate, the “sponsored” results could be amazing products that you will purchase, love and even re-order in the future. The data bias written into the “Best Sellers” calculation could absolutely be favoring great products you will know and love. However, it’s critical that we don’t turn a blind eye to the bias and we continue to scroll past the “sponsored” and “best sellers” to get a full picture for purchasing.
The objective of this article isn’t to get you to stop buying from Amazon but rather to consider the data bias right in front of you. If there is this much data bias in one simple Amazon search, how many more things in your life are impacted by data bias? How much is your business and its bottom line impacted by data bias? Algorithms, AI and their inherent bias are a part of daily life. How will you use them to create change for good?
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