[23480] in Privacy_Forum
[ PRIVACY Forum ] Script of my national radio report yesterday on a
daemon@ATHENA.MIT.EDU (Lauren Weinstein)
Tue Apr 14 11:19:58 2026
Date: Tue, 14 Apr 2026 08:10:14 -0700
From: Lauren Weinstein <lauren@vortex.com>
To: privacy-dist@vortex.com
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This is the script of my national network radio report yesterday on a
recent study of errors in Google Search AI Overviews. As always,
there may have been minor wording variations from this script as I
presented this report live on air.
- - -
So we've talked in the past about inaccuracies in Google's Search AI
Overviews (let's call them AIOs) that so often seem to provide
specific answers to search queries, usually in rather authoritative
wording. And there have been many concerns about how accurate or not
these AIOs actually are. I've in the past noted here a number of
examples where AIOs were just flat out wrong even with simple math
questions, which certainly doesn't give one much confidence in more
complex kinds of queries.
Now a firm called Oumi [OOH-MEE] led by a team of former-Google and
former-Apple employees has conducted a study that, combined with what
Google does say about these issues, gives us considerable insight into
some real statistics about Google AIOs. And those numbers are quite
disturbing.
Of course Google has disclaimers all over the place saying that AI
answers could be wrong and you should double-check them. But other
studies have shown that hardly anybody bothers to do that. And why
would you expect them to? What's the point of being handed an "answer"
if you can't trust that's it's correct? And what does "correct" even
really mean? Some answers from Google AIOs are fully correct, some are
completely wrong, and some are only PARTLY correct -- this last case
can be particularly insidious. Even when Google AIOs provide links to
what are supposedly the source sites related to an answer, often those
sites had the information wrong themselves, and then Google
unfortunately accepted it as correct and just stirred it into an AIO
answer. Sometimes when you look you can't even find where on those
sites Google supposedly got that information at all -- it just doesn't
seem to be there.
So what's the scope of this problem as determined by this new study?
Keep in mind this IS a tough nut to crack, for a bunch of reasons, one
being Google AIOs are not particularly consistent. In fact, you can
ask the same question twice just seconds apart, and, as you may have
noticed you may get entirely different and even completely
contradictory answers.
Another foundational point is that percentages alone don't tell the
whole story. The massive scale of Google -- they have BILLIONS of
users across their ecosystem -- is absolutely crucial to consider.
This applies whether we're talking about their continuing account
recovery policy failures when it comes to innocent users losing access
to their accounts, or when it comes to this question of AIOs. The
point is, at Google scale even relatively small percentages will
represent vast numbers of actual human beings.
Summarizing the numbers it shakes out like this. There are a lot of
factors involved but let's be generous and say that AIOs are
inaccurate overall about 9% of the time. There are significantly
larger inaccuracy numbers when you look at specific related Google
Gemini AI statistics and whether particular answers seem to be
ungrounded as relates to referenced sites or inaccuracies at those
sites.
Now let's cut to the chase. At Google scale of around 5 TRILLION
search queries a year -- that's trillions with a "T" -- the study says
that we're dealing with on the order of 10s of millions of wrong
Google AIO answers EVERY HOUR, hundreds of thousands PER MINUTE. To
me, especially when you consider the impracticality of really
double-checking such answers, this is a terrible situation.
Google has become a misinformation machine on a colossal scale,
virtually a mirror image of the trust we used to be able to put in
their traditional search engine results without AIOs. Because it's NOT
the CORRECT answers we have to worry about, it's the damage
potentially done by wrong or partly wrong answers that people
nonetheless understandably accept and act upon as if those answers
were correct.
SCALE MATTERS, and enormous scale like Google's matters enormously.
But it's the human beings around the world being impacted by that
scale who Google should care about. The situation is made even worse
since Google really won't take responsibility for inaccuracies in
their AIOs impacting those persons. Google is making money hand over
fist, but when it comes to really caring about their users, they're
failing more than ever.
- - -
L
- - -
--Lauren--
Lauren Weinstein
lauren@vortex.com (https://www.vortex.com/lauren)
Lauren's Blog: https://lauren.vortex.com
Mastodon: https://mastodon.laurenweinstein.org/@lauren
Signal: By request on need to know basis
Founder: Network Neutrality Squad: https://www.nnsquad.org
PRIVACY Forum: https://www.vortex.com/privacy-info
Co-Founder: People For Internet Responsibility
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