  There is a growing concern that e-commerce platforms are amplifying
vaccine-misinformation. To investigate, we conduct two-sets of algorithmic
audits for vaccine misinformation on the search and recommendation algorithms
of Amazon -- world's leading e-retailer. First, we systematically audit
search-results belonging to vaccine-related search-queries without logging into
the platform -- unpersonalized audits. We find 10.47% of search-results promote
misinformative health products. We also observe ranking-bias, with Amazon
ranking misinformative search-results higher than debunking search-results.
Next, we analyze the effects of personalization due to account-history, where
history is built progressively by performing various real-world user-actions,
such as clicking a product. We find evidence of filter-bubble effect in
Amazon's recommendations; accounts performing actions on misinformative
products are presented with more misinformation compared to accounts performing
actions on neutral and debunking products. Interestingly, once user clicks on a
misinformative product, homepage recommendations become more contaminated
compared to when user shows an intention to buy that product.
