Beyond the Algorithm: Why Smart AI Governance Looks at the Bigger Picture

We're all getting more clued into the need for AI governance. Often, the spotlight's on building "safe models"—making sure the AI tool itself behaves as expected. But think about it: a super-safe power drill can still cause a lot of damage in the hands of someone who doesn't know how to use it, right? Similarly, even a perfectly aligned AI can stumble and cause issues if it's dropped into a messy, unclear, or unprepared system.

The recent piece from Tech Policy Press, "Beyond Safe Models: Why AI Governance Must Tackle Unsafe Ecosystems," really hits this home and makes me think about how we're thinking about governing AI in the US. It argues that while the world is rightly focusing on AI safety at the model level, some of the most immediate risks pop up not from the AI itself, but from how it operates in the real world—when it's plugged into companies or organizations with conflicting goals, weak oversight, or just not enough preparation.

The EU AI Act is laying some important groundwork with rules and restrictions, but like a lot of current efforts, it mostly looks at the AI model's features, not so much at the wild environment it's going into. To really govern AI effectively, we need to zoom out and look at the safety of the whole ecosystem.

These deployment risks aren't just thought experiments. Consider these real-world scenarios:

• The Social Media Rabbit Hole: Those recommendation systems on social media? Technically, they're pretty slick—designed to keep you engaged. But we've seen how they can amplify extreme views and fake news. The problem isn't a bug in the algorithm's code, but the platform's drive to grab your attention at any cost.

• Hiring Bias by Algorithm: AI tools used in hiring have shown racial and gender bias, even when they meet technical standards. One system actually ranked candidates lower for having gone to women's colleges! This wasn't a technical glitch; it learned bias from past hiring decisions and was used without enough careful oversight or a way to challenge its decisions.

In both these cases, the underlying AI models might have passed their technical checks. But when they're unleashed in high-stakes, unclear situations with messed-up incentives, they can deliver outcomes that are totally unfair or even harmful.

Stepping Back: From Safe Models to Safe Ecosystems

Despite these obvious risks of deploying AI into less-than-ideal environments, the main way we think about AI governance still leans heavily on what happens before deployment—things like making sure the AI is aligned with our values, can explain its reasoning, and has been tested for flaws. Initiatives like the EU AI Act, while definitely important, mostly put the burden on the developers and providers to show they've done their homework with documentation, transparency, and risk management plans.

However, we pay comparatively less attention to governing what happens after these AI models are out in the wild, plugged into organizations with their own agendas, infrastructures, and ways of checking things.

For example, while the EU AI Act does mention post-market monitoring and responsibilities for those using high-risk AI, these are still pretty limited. Monitoring mainly looks at technical stuff, not so much at the broader impact on society or how it changes things.

And the responsibilities for those using the AI are more about following procedures—keeping records and having a human in the loop—rather than really checking if the organization has the ability, the right motivations, or the safeguards to use the AI responsibly. So, there's not a lot of guarantee that AI systems will end up in places that can actually handle the risks that come up.

Yet, as we saw with the biased hiring AI and the polarizing social media, it's in this real-world "deployment ecosystem" where a lot of the trouble actually brews. This ecosystem is a tangled web of the organizations using the AI, what they're trying to achieve (like being more efficient, getting more clicks, or making more money), the tech and people supporting its use, and the laws, rules, and social norms around it.

Like any ecosystem, everything's connected. If, for example, the people using the AI aren't trained well because the company cares more about speed than preparation, or if the public can't challenge automated decisions because it's all so confusing and no one knows who's really in charge, then just having a technically safe AI isn't going to stop bad things from happening down the line.

So, AI governance needs to ask: Where is this AI being used? By whom? For what purpose? And who's keeping an eye on it? We've got to move beyond just checking the AI model before it's deployed and build a strong system that puts the safety of this whole deployment ecosystem at the center of how we evaluate risk.

A Framework for Looking at the Real World

To help shift our focus, the article points out four key things about these deployment ecosystems that can either make AI risks worse or help manage them:

1. Do the Goals Line Up? Governance needs to think about whether the organizations using AI are prioritizing the public good or just chasing short-term wins like profit or clicks. Even a technically perfect AI can cause harm if it's used in a place where the incentives reward manipulation or taking advantage of people. While the EU AI Act does regulate some specific uses and assigns risk levels, it doesn't really dig into the motivations of the companies using the AI—leaving a big gap in how we understand real-world risk.

2. Is the Environment Ready? Not every organization is equally set up to handle the risks of AI. Things like having good laws in place (so people can challenge decisions), strong tech systems, the ability to bounce back from problems, and whether the people using the AI actually understand it all play a big role in how responsibly an AI can be used. A technically safe AI dropped into a place without good regulations or social support can still cause widespread harm. The EU AI Act rightly flags high-risk areas, but it doesn't really check if the organizations using AI there are equipped to manage those risks. For instance, a big company with its own legal team and audit processes is very different from a small HR startup using an off-the-shelf AI tool with hardly any oversight—yet they might fall under the same risk category.

3. Who's Accountable and What's Visible? Organizations using AI should be set up to be responsible, open to challenge, and fair. That means clear lines of who's in charge, ways for people to question decisions, and knowing who benefits and who takes the risks. Without this transparency and the ability to seek solutions when things go wrong, even technically compliant systems can make existing power imbalances worse and erode public trust. For example, while the EU AI Act has some procedural safeguards, it doesn't really guarantee people can challenge decisions—it might offer explanations for high-risk AI decisions but no clear way to overturn them, making accountability fuzzy and real solutions limited.

4. Can We Adapt to New Risks? AI systems interact with the real world, which is always changing, and they can produce effects we didn't see coming when they were first used. So, governance needs to be ongoing—we need to watch what happens in the real world and be ready to respond to new risks as they appear. While the EU AI Act requires some post-market monitoring for high-risk systems, it's often driven by the AI providers and focuses on technical compliance and major incidents, not so much on broader societal harms or long-term impacts. As AI keeps evolving and being used in more and more different ways, we need clear ways to spot and address the risks that come from these specific uses and situations.

Wrapping Up

Ultimately, we don't just need AI that works correctly in a lab. We need to make sure that the whole system around AI is safe. As AI becomes more and more a part of our lives, the dangers aren't just in flawed code, but in the things we don't see or question about the world we're unleashing AI into: the incentives we ignore, the contexts we don't evaluate, and the harms we only notice when it's too late. Broadening our view of AI governance to really include the safety of the deployment ecosystem is essential. What makes AI risky isn't just what it can do, but also what we fail to ask about the world it's entering.

Given the EU's proactive steps in this direction, it begs the question: As the US charts its own course in AI governance, how will it ensure that the focus extends beyond just the technical soundness of AI models to address the crucial safety of the real-world ecosystems in which they are deployed?

~Wendy

Source Article: https://www.techpolicy.press/beyond-safe-models-why-ai-governance-must-tackle-unsafe-ecosystems/