The Risks of Non-Compliance in AI and How to Mitigate Them

Governments worldwide are working quickly to establish standards that regulate artificial intelligence. How can developers and tech professionals avoid non-compliance in a rapidly evolving regulatory landscape?

What Are the Main Existing AI Regulations?

Existing regulatory standards cover data sourcing, training, and model utilization. Most say developers should prevent harm, secure systems, and protect consumer privacy. While some are voluntary, others aren’t — meaning non-compliance can come with a steep price.

In the U.S., AI regulations have been a hot topic. President Biden issued an executive order in 2023 stating developers must share critical information with the government and ensure their models are trustworthy, private, secure, and safe before public release.

Around the same time, the Biden administration prompted the Department of Commerce and the National Institute of Standards and Technology (NIST) to develop legal and ethical frameworks for AI. They address the responsible use of algorithmic systems, tools, and services.

The European Union has some of the toughest AI regulations in the world. The EU IA Act bans unacceptable risks, requires model registration, and sets transparency standards. Since it passed in 2023 and has a two-year grace period, developers won’t have to follow it until 2026.

International policies exist, so no place is free from regulation. The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) created ISO/IEC 42001. Although it’s voluntary, it’s widely accepted as a standard for AI development.

What Does Non-Compliance in AI Look Like?

While most people are familiar with biased training data sets and skewed model output, many don’t know about the intricacies of non-compliance in AI. Often, it’s more complicated than it seems because there are so many moving parts.

Your AI might be non-compliant if it discriminates, libels someone, explains illegal activities, references a real person, or trains on material that isn’t fair use. Essentially, that means you must consider consumer privacy, data security, and copyright regulations during development.

Potential Legal and Ethical Risks of Non-Compliant AI

There are dozens of legal and ethical risks linked to developing or deploying a potentially non-compliant AI. You can get into trouble even without an audit from a regulatory agency — users can file a complaint if they think your model isn’t following the rules.

If an AI system doesn’t follow security standards, it becomes a target for threat actors. They could steal your intellectual property and user data, potentially resulting in lengthy legal battles and long-term financial damage.

At the very least, deploying a non-compliant AI means your model might act in harmful, unintended ways. It could discriminate against specific demographics, offer misinformation, or provide bad insights.

You risk worsening business outcomes if your AI is for internal use. If it’s consumer-facing, you face backlash and reputational damage. Either way, your investment in this promising emerging technology would quickly sour.

The most obvious repercussion for non-compliance is a financial penalty — most regulatory agencies won’t hesitate to hand out fines. For instance, the EU AI Act would make you pay €7.5-€35 million for a violation. Unfortunately, the consequences don’t end there.

Legal and ethical non-compliance opens you up to legal action. In the worst-case scenario, a regulatory agency or judicial system shuts down your AI, preventing you from developing another one. That effectively bars you from one of the most promising modern technologies.

How to Navigate the Complexities of AI Compliance

Being strategic about development and deployment is one of the few ways to navigate the complexities.

Transparency

Explainable AI can trace its decision-making process, effectively eliminating the common “black box” issue. This transparency simplifies reporting requirements. It can also help you spot anomalous — potentially non-compliant — behavior in time.

Privacy

Navigating data privacy can be challenging since you must follow consumer privacy and AI regulations simultaneously. Your first move should be to catalog your storage systems since the location of cloud servers and data centers affects which rules you’re subject to.

Security

You should always seek to improve your cybersecurity posture. No matter what, the data you feed from your AI or collect from your users should be reasonably safe from tampering, cyber attacks, and human error. Extensive red-team testing can help you identify security gaps.

Fairness

Bias prevention should be one of your priorities. You should source your data sets from relevant, reputable sources and verify their integrity before feeding them to your model. Make sure you train it on diverse resources to ensure its output is fair and impartial.

Best Practices for Mitigating Non-Compliant AI Risks

Numerous ways to effectively mitigate legal and ethical non-compliance risks exist.

1. Validate Data Quality

Data validation is one of the most important best practices for mitigating non-compliance in AI. You should collect your information from reputable sources. Additionally, you should filter it to catch any anomalies, malicious injections, or biased information.

2. Establish an Ethics Framework

An AI ethics framework is a set of policies that guide development and deployment. Even though you already have regulations to follow, building an internal structure strengthens your culture of responsibility, transparency, and morality, helping you stay compliant.

3. Implement Audit Trails

An audit trail is a chronological file of user or model actions complete with time stamps and dates. It can tell you exactly when someone created a backup, changed configurations, or made modifications. This kind of documentation is a life-saver when you’re bound to regulations with reporting requirements.

4. Conduct Impact Assessments

Human, social, economic, and environmental impact assessments look at the potential positive and negative effects of your AI — job losses and carbon output, for example. They help you see the big picture, which enables you to identify and fix problem areas.

5. Develop an Accountability Structure

Non-compliance is a big deal for any organization, so it often leads to a lot of finger-pointing. Developing a structure of accountability prevents that from happening. Since it clearly defines everyone’s responsibilities, it motivates them to remain compliant.

Mitigating Non-Compliance in AI Is Possible

While AI regulations can be confusing and overly complicated, remaining consistently compliant is possible. As long as you establish structures to support your legal and ethical responsibilities, you can mitigate non-compliance.