centralized social networks like Mastodon, combines content sharing with
built-in attribution. Tim Berners-Lee's Solid protocol restructures the Web
around personal data pods with granular access controls.

These technologies prioritize integrity through cryptographic verification that
proves authorship, decentralized architectures that eliminate vulnerable central
authorities, machine-readable semantics that make meaning explicit -- structured
data formats that allow computers to understand participants and actions, such
as "Alice performed surgery on Bob" -- and transparent governance where rules
are visible to all. As AI systems become more autonomous, communicating directly
with one another via standardized protocols, these integrity controls will be
essential for maintaining trust.

Why Data Integrity Matters in AI

For AI systems, integrity is crucial in four domains. The first is decision
quality. With AI increasingly contributing to decision-making in health care,
justice, and finance, the integrity of both data and models' actions directly
impact human welfare. Accountability is the second domain. Understanding the
causes of failures requires reliable logging, audit trails, and system records.

The third domain is the security relationships between components. Many
authentication systems rely on the integrity of identity information and
cryptographic keys. If these elements are compromised, malicious agents could
impersonate trusted systems, potentially creating cascading failures as AI
agents interact and make decisions based on corrupted credentials.

Finally, integrity matters in our public definitions of safety. Governments
worldwide are introducing rules for AI that focus on data accuracy, transparent
algorithms, and verifiable claims about system behavior. Integrity provides the
basis for meeting these legal obligations.

The importance of integrity only grows as AI systems are entrusted with more
critical applications and operate with less human oversight. While people can
sometimes detect integrity lapses, autonomous systems may not only miss warning
signs -- they may exponentially increase the severity of breaches. Without
assurances of integrity, organizations will not trust AI systems for important
tasks, and we won't realize the full potential of AI.

How to Build AI Systems With Integrity

Imagine an AI system as a home we're building together. The integrity of this
home doesn't rest on a single security feature but on the thoughtful integration
of many elements: solid foundations, well-constructed walls, clear pathways
between rooms, and shared agreements about how spaces will be used.

We begin by laying the cornerstone: cryptographic verification. Digital
signatures ensure that data lineage is traceable, much like a title deed proves
ownership. Decentralized identifiers act as digital passports, allowing
components to prove identity independently. When the front door of our AI home
recognizes visitors through their own keys rather than through a vulnerable
central doorman, we create resilience in the architecture of trust.

Formal verification methods enable us to mathematically prove the structural
integrity of critical components, ensuring that systems can withstand pressures
placed upon them -- especially in high-stakes domains where lives may depend on
an AI's decision.

Just as a well-designed home creates separate spaces, trustworthy AI systems are
built with thoughtful compartmentalization. We don't rely on a single barrier
but rather layer them to limit how problems in one area might affect others.
Just as a kitchen fire is contained by fire doors and independent smoke alarms,
training data is separated from the AI's inferences and output to limit the
impact of any single failure or breach.

Throughout this AI home, we build transparency into the design: The equivalent
of large windows that allow light into every corner is clear pathways from input
to output. We install monitoring systems that continuously check for weaknesses,
alerting us before small issues become catastrophic failures.

But a home isn't just a physical structure, it's also the agreements we make
about how to live within it. Our governance frameworks act as these shared
understandings. Before welcoming new residents, we provide them with
certification standards. Just as landlords conduct credit checks, we conduct
integrity assessments to evaluate newcomers. And we strive to be good neighbors,
aligning our community agreements with broader societal expectations. Perhaps
most important, we recognize that our AI home will shelter diverse individuals
with varying needs. Our governance structures must reflect this diversity,
bringing many stakeholders to the table. A truly trustworthy system cannot be
designed only for its builders but must serve anyone authorized to eventually
call it home.

That's how we'll create AI systems worthy of trust: not by blindly believing in
their perfection but because we've intentionally designed them with integrity
controls at every level.

A Challenge of Language

Unlike other properties of security, like "available" or "private," we don't
have a common adjective form for "integrity." This makes it hard to talk about
it. It turns out that there is a word in English: "integrous." The Oxford
English Dictionary recorded the word used in the mid-1600s but now declares it
obsolete.

We believe that the word needs to be revived. We need the ability to describe a
system with integrity. We must be able to talk about integrous systems design.

The Road Ahead

Ensuring integrity in AI presents formidable challenges. As models grow larger
and more complex, maintaining integrity without sacrificing performance becomes
difficult. Integrity controls often require computational resources that can
slow systems down -- particularly challenging for real-time applications.
Another concern is that emerging technologies like quantum computing threaten
current cryptographic protections. Additionally, the distributed nature of
modern AI -- which relies on vast ecosystems of libraries, frameworks, and
services -- presents a large attack surface.

Beyond technology, integrity depends heavily on social factors. Companies often
prioritize speed to market over robust integrity controls. Development teams may
lack specialized knowledge for implementing these controls, and may find it
particularly difficult to integrate them into legacy systems. And while some
governments have begun establishing regulations for aspects of AI, we need
worldwide alignment on governance for AI integrity.

Addressing these challenges requires sustained research into verifying and
enforcing integrity, as well as recovering from breaches. Priority areas include
fault-tolerant algorithms for distributed learning, verifiable computation on
encrypted data, techniques that maintain integrity despite adversarial attacks,
and standardized metrics for certification. We also need interfaces that clearly
communicate integrity status to human overseers.

As AI systems become more powerful and pervasive, the stakes for integrity have
never been higher. We are enteri

--- BBBS/LiR v4.10 Toy-7
 * Origin: TCOB1: https/binkd/telnet binkd.rima.ie (618:500/1)