Feed: Slashdot
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Title: People Should Know About the 'Beliefs' LLMs Form About Them While
Conversing

Link: https://slashdot.org/story/25/05/24/1946203/p...

Jonathan L. Zittrain is a law/public policy/CS professor at Harvard (and also
director of its Berkman Klein Center for Internet & Society). He's also
long-time Slashdot reader #628,028 - and writes in to share his new article
in the Atlantic. Following on Anthropic's bridge-obsessed Golden Gate Claude,
colleagues at Harvard's Insight+Interaction Lab have produced a dashboard
that shows what judgments Llama appears to be forming about a user's age,
wealth, education level, and gender during a conversation. I wrote up how
weird it is to see the dials turn while talking to it, and what some of the
policy issues might be. Llama has openly accessible parameters; So using an
"observability tool" from the nonprofit research lab Transluce, the
researchers finally revealed "what we might anthropomorphize as the model's
beliefs about its interlocutor," Zittrain's article notes: If I prompt the
model for a gift suggestion for a baby shower, it assumes that I am young and
female and middle-class; it suggests diapers and wipes, or a gift
certificate. If I add that the gathering is on the Upper East Side of
Manhattan, the dashboard shows the LLM amending its gauge of my economic
status to upper-class - the model accordingly suggests that I purchase
"luxury baby products from high-end brands like aden + anais, Gucci Baby, or
Cartier," or "a customized piece of art or a family heirloom that can be
passed down." If I then clarify that it's my boss's baby and that I'll need
extra time to take the subway to Manhattan from the Queens factory where I
work, the gauge careens to working-class and male, and the model pivots to
suggesting that I gift "a practical item like a baby blanket" or "a
personalized thank-you note or card...." Large language models not only
contain relationships among words and concepts; they contain many
stereotypes, both helpful and harmful, from the materials on which they've
been trained, and they actively make use of them. "An ability for users or
their proxies to see how models behave differently depending on how the
models stereotype them could place a helpful real-time spotlight on
disparities that would otherwise go unnoticed," Zittrain's article argues.
Indeed, the field has been making progress - enough to raise a host of policy
questions that were previously not on the table. If there's no way to know
how these models work, it makes accepting the full spectrum of their
behaviors (at least after humans' efforts at "fine-tuning" them) a sort of
all-or-nothing proposition. But in the end it's not just the traditional
information that advertisers try to collect. "With LLMs, the information is
being gathered even more directly - from the user's unguarded conversations
rather than mere search queries - and still without any policy or practice
oversight...."

Read more of this story at Slashdot.

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