. In the same way, while scientists and technologists should anticipate, warn
against, and help mitigate the potential harms of AI, they should also highlight
the ways the technology can be harnessed for good, galvanizing public action
towards those ends.

There are myriad ways to leverage and reshape AI to improve peoples? lives,
distribute rather than concentrate power, and even strengthen democratic
processes. Many examples have arisen from the scientific community and deserve
to be celebrated.

Some examples: AI is eliminating communication barriers across languages,
including under-resourced contexts like marginalized sign languages and
indigenous African languages. It is helping policymakers incorporate the
viewpoints of many constituents through AI-assisted deliberations and
legislative engagement. Large language models can scale individual dialogs to
address climate -- change skepticism, spreading accurate information at a
critical moment. National labs are building AI foundation models to accelerate
scientific research. And throughout the fields of medicine and biology, machine
learning is solving scientific problems like the prediction of protein structure
in aid of drug discovery, which was recognized with a Nobel Prize in 2024.

While each of these applications is nascent and surely imperfect, they all
demonstrate that AI can be wielded to advance the public interest. Scientists
should embrace, champion, and expand on such efforts.

A Call to Action for Scientists

In our new book, Rewiring Democracy: How AI Will Transform Our Politics,
Government, and Citizenship, we describe four key actions for policymakers
committed to steering AI toward the public good.

These apply to scientists as well. Researchers should work to reform the AI
industry to be more ethical, equitable, and trustworthy. We must collectively
develop ethical norms for research that advance and applies AI, and should use
and draw attention to AI developers who adhere to those norms.

Second, we should resist harmful uses of AI by documenting the negative
applications of AI and casting a light on inappropriate uses.

Third, we should responsibly use AI to make society and peoples? lives better,
exploiting its capabilities to help the communities they serve.

And finally, we must advocate for the renovation of institutions to prepare them
for the impacts of AI; universities, professional societies, and democratic
organizations are all vulnerable to disruption.

Scientists have a special privilege and responsibility: We are close to the
technology itself and therefore well positioned to influence its trajectory. We
must work to create an AI-infused world that we want to live in. Technology, as
the historian Melvin Kranzberg observed, ?is neither good nor bad; nor is it
neutral.? Whether the AI we build is detrimental or beneficial to society
depends on the choices we make today. But we cannot create a positive future
without a vision of what it looks like.

This essay was written with Nathan E. Sanders, and originally appeared in IEEE
Spectrum.

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Rigged Poker Games

[2025.11.06] The Department of Justice has indicted thirty-one people over the
high-tech rigging of high-stakes poker games.

In a typical legitimate poker game, a dealer uses a shuffling machine to shuffle
the cards randomly before dealing them to all the players in a particular order.
As set forth in the indictment, the rigged games used altered shuffling machines
that contained hidden technology allowing the machines to read all the cards in
the deck. Because the cards were always dealt in a particular order to the
players at the table, the machines could determine which player would have the
winning hand. This information was transmitted to an off-site member of the
conspiracy, who then transmitted that information via cellphone back to a member
of the conspiracy who was playing at the table, referred to as the ?Quarterback?
or ?Driver.? The Quarterback then secretly signaled this information (usually by
prearranged signals like touching certain chips or other items on the table) to
other co-conspirators playing at the table, who were also participants in the
scheme. Collectively, the Quarterback and other players in on the sch
eme (i.e., the cheating team) used this information to win poker games against
unwitting victims, who sometimes lost tens or hundreds of thousands of dollars
at a time. The defendants used other cheating technology as well, such as a chip
tray analyzer (essentially, a poker chip tray that also secretly read all cards
using hidden cameras), an x-ray table that could read cards face down on the
table, and special contact lenses or eyeglasses that could read pre-marked
cards.

News articles.

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Faking Receipts with AI

[2025.11.07] Over the past few decades, it?s become easier and easier to create
fake receipts. Decades ago, it required special paper and printers -- I remember
a company in the UK advertising its services to people trying to cover up their
affairs. Then, receipts became computerized, and faking them required some
artistic skills to make the page look realistic.

Now, AI can do it all:

Several receipts shown to the FT by expense management platforms demonstrated
the realistic nature of the images, which included wrinkles in paper, detailed
itemization that matched real-life menus, and signatures.

[...]

The rise in these more realistic copies has led companies to turn to AI to help
detect fake receipts, as most are too convincing to be found by human reviewers.

The software works by scanning receipts to check the metadata of the image to
discover whether an AI platform created it. However, this can be easily removed
by users taking a photo or a screenshot of the picture.

To combat this, it also considers other contextual information by examining
details such as repetition in server names and times and broader information
about the employee?s trip.

Yet another AI-powered security arms race.

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New Attacks Against Secure Enclaves

[2025.11.10] Encryption can protect data at rest and data in transit, but does
nothing for data in use. What we have are secure enclaves. I?ve written about
this before:

Almost all cloud services have to perform some computation on our data. Even the
simplest storage provider has code to copy bytes from an internal storage system
and deliver them to the user. End-to-end encryption is sufficient in such a
narrow context. But often we want our cloud providers to be able to perform
computation on our raw data: search, analysis, AI model training or fine-tuning,
and more. Without expensive, esoteric techniques, such as secure multiparty
computation protocols or homomorphic encryption techniques that can perform
calculations on encrypted data, cloud servers require access to the unencrypted
data to do anything useful.

Fortunately, the last few years have seen the advent of general-purpose,
hardware-enabled secure computation. This is powered by special functionality on
pro

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