When people talk about AI alignment, they’re talking about a simple issue: how do we make sure that what AI systems optimize for is consistent with human values, organizational goals, and societal expectations?
Left unaddressed, this gap between what we intend and what machines actually do can create risks. Misaligned AI can lead to biased outcomes, compliance failures, reputational damage, or even erosion of user trust. For organizations deploying AI, the alignment problem is a business problem that needs to be addressed as a core part of the operating model.
What Artificial Integrity Means
Hamilton Mann‘s recent work on Artificial Integrity offers a useful way of thinking about this problem. The core idea is that it isn’t enough for AI to be intelligent; it must also act with integrity.
Mann frames “Artificial Integrity” as the set of principles, safeguards, and cultural checks that keep AI systems aligned with human interests. Without it, users can fall into what he calls “Technological Stockholm Syndrome,” where we slowly adapt ourselves to the logic of machines instead of the other way around.
At a practical level, Artificial Integrity means addressing gaps that are all too common in AI systems: lack of transparency, missing safeguards, addictive design patterns, cultural conflicts, or unchecked bias. Filling these gaps will strengthens the foundation for long-term, trustworthy AI adoption.
Artificial Integrity and EU Regulations
This way of thinking dovetails neatly with the EU Artificial Intelligence Act, which is built around a risk-based framework. High-risk AI systems (such as those used in healthcare, employment, or critical infrastructure) must meet requirements for transparency, oversight, and safety. Some uses of AI are prohibited outright.
Artificial Integrity provides organizations with a design philosophy that makes regulatory compliance more manageable. By focusing on the functional gaps Mann identifies: bias, explainability, safeguards, ethical annotations, amongst others; companies can move beyond bare-minimum compliance and toward systems that both meet EU requirements and earn user trust.
The Role of Compliance Engineering
This is where compliance engineering comes into play. Specially trained engineers and data scientists already know how to annotate data for fairness, design transparent models, and build feedback loops that evolve with shifting norms. What’s needed is a structured way to make those practices part of the development process from the start.
Compliance engineering treats regulations, ethics, and integrity not as afterthoughts but as inputs to system design. The result is AI that is not only more resilient against regulatory scrutiny, but also more reliable in day-to-day use.
We see Artificial Integrity as one of the two keys to meaningful AI alignment. The other is something we’re developing separately, which we call a **Universal Ethical Cost Function**. Taken together, these approaches reflect our belief that ethics engineering and compliance engineering go hand in hand, and that both will be essential in the 21st century.
Artificial Intelligence will only be as good as the integrity we build into it. That work is deliberate, but achievable, and it starts with recognizing alignment not just as a technical challenge but as a cultural and organizational one.
- Disclosures: This article is not in any way sponsored.