The 'bias machine': How Google tells you what you want to hear
www.bbc.com
"We're at the mercy of Google." Undecided voters in the US who turn to Google may see dramatically different views of the world – even when they're asking the exact same question.

Some experts say Google is just parroting your own beliefs right back to you. It may be worsening your own biases and deepening societal divides along the way.

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“Google’s whole mission is to give people the information that they want, but sometimes the information that people think they want isn’t actually the most useful,” says Sarah Presch, digital marketing director at Dragon Metrics, a platform that helps companies tune their websites for better recognition from Google using methods known as “search engine optimisation” or SEO.

[…]

“> What Google has done is they’ve pulled bits out of the text based on what people are searching for and fed them what they want to read” – Sarah Presch

  • kbal@fedia.io
    18·
    2 months ago

    The Featured Snippet quoted an article from the Mayo Clinic, highlighting the words “Caffeine may cause a short, but dramatic increase in your blood pressure.” But when she looked up “no link between coffee and hypertension”, the Featured Snippet cited a contradictory line from the very same Mayo Clinic article: “Caffeine doesn’t have a long-term effect on blood pressure and is not linked with a higher risk of high blood pressure”.

    On the one hand, Google sucks. On the other hand, if people are unable to a) understand how those two snippets are not contradictory, and b) read at least one very short simplified-for-laymen Mayo Clinic article about the topic before thinking they’ve learned anything at all about medicine, it’s hard to see the problem as being primarily due to Google. There is something deeper, and worse, going wrong when people habitually take that kind of extreme shortcut to thinking that they know the right answer about almost anything, and it has little to do with whether any one-sentence snippets they’re given are biased or accurate.