How to Find Your Amazon 'About You' Page and Edit What It Knows About You
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The online uproar began when shoppers began sharing screenshots of their profiles on forums and social video networks. While some expected to see mundane details like home addresses or clothing sizes, many found bizarre, highly specific observational tags. Reporting by Fast Company highlighted users discovering algorithmic tags such as "has flat buttocks" or notes claiming the user "cusses when scared." These blunt, uncensored fragments looked less like polished marketing data and more like raw internal system notes.
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Online communities reacted with a mix of dark humor and genuine concern. Reddit threads dedicated to data privacy exploded with thousands of shoppers digging into their own profiles. Many discovered tags indicating private medical assumptions, household size estimates, and behavioral quirks derived from decades of incidental shopping. What felt to users like an innocuous retail storefront had functioned as an active intelligence-gathering operation, logging casual interactions into a dossier meant to anticipate their next commercial move.
The phenomenon reveals an awkward disconnect in modern retail platforms. When machine learning engines process fragmented data, the internal labels they assign are often startlingly literal. A person purchasing supportive cushions for an aging office chair suddenly receives a permanent physical descriptor. Someone ordering prank gifts or novelty noise devices finds themselves classified under psychological profiles that follow them across the marketplace.
Marcus Vance is a cybersecurity auditor and technology writer dedicated to educating the public about online safety, data privacy regulations, enterprise security, and emerging cyber threats.
