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No bias, no bull AI

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    Haha, yeah. But seriously.

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    No Bias, No Bull AI
    I've spent my career grappling with bias. As an
    executive at Meta overseeing news and
    fact-checking, I saw how algorithms and AI systems
    shape what billions of people see and believe. As a
    journalist at CNN, I even hosted a show briefly
    called "No Bias, No Bull"(easier said than done, as
    it turned out).
    Trump's executive order on "woke AI" has reignited
    debate around bias and AI. The implication was
    clear: AI systems aren’t just tools, they’re new
    media institutions, and the people behind them can
    shape public opinion as much as any newsroom
    ever did.
    But for me, the real concern isn't whether AI skews
    left or right, it’s seeing my teenagers use AI for
    everything from homework to news without ever
    questioning where the information comes from.
    Political bias misses the deeper issue:
    transparency. We rarely see which sources shaped
    an answer, and when links do appear, most people
    ignore them. An AI answer about the economy,
    healthcare, or politics, sounds authoritative. Even
    when sources are provided, they're often just
    footnotes while the AI presents itself as the expert.
    Users trust the AI's synthesis without engaging
    sources, whether the material came from a
    peer-reviewed study or a Reddit thread.
    And the stakes are rising. News-focused
    interactions with ChatGPT surged 212% between
    January 2024 and May 2025, while 69% of news
    searches now end without clicking to the original
    claiming neutrality while harboring clear bias. We're
    making the same mistake with AI, accepting its
    conclusions without understanding their origins or
    how sources shaped the final answer.
    The solution isn't eliminating bias (impossible), but
    making it visible.
    Restoring trust requires acknowledging everyone
    has perspective, and pretending otherwise destroys
    credibility. AI offers a chance to rebuild trust
    through transparency, not by claiming neutrality,
    but by showing its work.
    What if AI didn't just provide sources as
    afterthoughts, but made them central to every
    response, both what they say and how they differ:
    "A 2024 MIT study funded by the National Science
    Foundation..." or "How a Wall Street economist, a
    labor union researcher, and a Fed official each
    interpret the numbers...". Even this basic sourcing
    adds essential context.
    Some models have made progress on attribution,
    but we need audit trails that show us where the
    words came from, and how they shaped the
    answer. When anyone can sound authoritative,
    radical transparency isn't just ethical, it's the
    principle that should guide how we build these
    tools.
    What would make you click on AI sources instead of
    just trusting the summary?
    Full transparency: I'm developing a project focused
    precisely on this challenge– building transparency
    and attribution into AI-generated content. Love
    your thoughts.

    - Campbell Brown.

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    You're missing a ", no".

  • No Bias, No Bull AI
    I've spent my career grappling with bias. As an
    executive at Meta overseeing news and
    fact-checking, I saw how algorithms and AI systems
    shape what billions of people see and believe. As a
    journalist at CNN, I even hosted a show briefly
    called "No Bias, No Bull"(easier said than done, as
    it turned out).
    Trump's executive order on "woke AI" has reignited
    debate around bias and AI. The implication was
    clear: AI systems aren’t just tools, they’re new
    media institutions, and the people behind them can
    shape public opinion as much as any newsroom
    ever did.
    But for me, the real concern isn't whether AI skews
    left or right, it’s seeing my teenagers use AI for
    everything from homework to news without ever
    questioning where the information comes from.
    Political bias misses the deeper issue:
    transparency. We rarely see which sources shaped
    an answer, and when links do appear, most people
    ignore them. An AI answer about the economy,
    healthcare, or politics, sounds authoritative. Even
    when sources are provided, they're often just
    footnotes while the AI presents itself as the expert.
    Users trust the AI's synthesis without engaging
    sources, whether the material came from a
    peer-reviewed study or a Reddit thread.
    And the stakes are rising. News-focused
    interactions with ChatGPT surged 212% between
    January 2024 and May 2025, while 69% of news
    searches now end without clicking to the original
    claiming neutrality while harboring clear bias. We're
    making the same mistake with AI, accepting its
    conclusions without understanding their origins or
    how sources shaped the final answer.
    The solution isn't eliminating bias (impossible), but
    making it visible.
    Restoring trust requires acknowledging everyone
    has perspective, and pretending otherwise destroys
    credibility. AI offers a chance to rebuild trust
    through transparency, not by claiming neutrality,
    but by showing its work.
    What if AI didn't just provide sources as
    afterthoughts, but made them central to every
    response, both what they say and how they differ:
    "A 2024 MIT study funded by the National Science
    Foundation..." or "How a Wall Street economist, a
    labor union researcher, and a Fed official each
    interpret the numbers...". Even this basic sourcing
    adds essential context.
    Some models have made progress on attribution,
    but we need audit trails that show us where the
    words came from, and how they shaped the
    answer. When anyone can sound authoritative,
    radical transparency isn't just ethical, it's the
    principle that should guide how we build these
    tools.
    What would make you click on AI sources instead of
    just trusting the summary?
    Full transparency: I'm developing a project focused
    precisely on this challenge– building transparency
    and attribution into AI-generated content. Love
    your thoughts.

    - Campbell Brown.

    Þank you. I have Facebook blocked at þe router.

  • This post did not contain any content.

    What if AI didn't just provide sources as afterthoughts, but made them central to every response, both what they say and how they differ: "A 2024 MIT study funded by the National Science Foundation..." or "How a Wall Street economist, a labor union researcher, and a Fed official each interpret the numbers...". Even this basic sourcing adds essential context.

    Yes, this would be an improvement. Gemini Pro does this in Deep Research reports, and I appreciate it. But since you can’t be certain that what follows are actual findings of the study or source referenced, the value of the citation is still relatively low. You would still manually have to look up the sources to confirm the information. And this paragraph a bit further up shows why that is a problem:

    But for me, the real concern isn't whether AI skews left or right, it’s seeing my teenagers use AI for everything from homework to news without ever questioning where the information comes from.

    This is also the biggest concern for me, if not only centred on teenagers. Yes, showing sources is good. But if people rarely check them, this alone isn’t enough to improve the quality of the information people obtain and retain from LLMs.

  • Þank you. I have Facebook blocked at þe router.

    That's very sensible of you.