10 steps to make sure your AI system meets regulatory standards

10 steps to ensure your ai System meets regulatory standards

When designing AI systems, meeting regulatory standards is essential in ensuring the safe and successful deployment of your AI system. Non-compliance with any regulations can lead to hefty fines, reputational damage, and even the shutdown of AI systems. Whether you’re developing AI in healthcare, finance, or any other domain, this guide will help ensure your system meets all necessary regulatory requirements.

This article will go through step by step how you can ensure your AI system meets all the required regulations. 

1. Understand Applicable Regulations

The first step in this process is to identify the regulations that apply to your AI system. Key regulations include: 

  • EU AI Act: Focused on risk classification and accountability.
  • GDPR (General Data Protection Regulation): Governs data privacy in AI systems operating in or affecting EU citizens.
  • U.S. Federal Trade Commission (FTC) Guidelines: Pertinent to fairness and transparency in AI algorithms.

Required Regulations usually depend on which country you are in. Research your countries AI and Data Regulations or view our guide here.

Familiarize yourself with sector-specific guidelines and keep up-to-date with changes in global standards.

2. Perform a Risk Assessment

Evaluate your AI system’s potential risks, including:

  • Bias and discrimination: Could your model produce unfair outcomes?
  • Security vulnerabilities: Are there risks of data breaches or adversarial attacks?
  • Transparency gaps: Can your model explain its decisions?

Document these risks and ensure mitigation strategies are in place.  Most regulations will require you to perform risk assessments to either categorise your AI system (EU AI Act) or to assess the potential harm this may cause to the public. This step is essential as it ensures your AI system does not cause any damage or disruption to any groups of people. 

3. Build Transparency Into Your System

Transparency is a cornerstone of AI compliance. Users must always be made aware they are interacting with AI generated material.  Follow these three steps to ensure transparency. 

  • Implement explainable AI (XAI) techniques to clarify how decisions are made.
  • Maintain detailed audit trails of model training, testing, and updates.
  • Provide user documentation that explains system limitations and operational parameters.

4. Prioritize Data Privacy and Security

AI and Data go hand in hand, therefore AI systems are only as ethical as the data they use. To comply with data protection laws:

  • Use anonymization or pseudonymization to protect sensitive data.
  • Conduct data mapping to understand where data originates and how it flows through your system.
  • Regularly perform penetration testing to secure AI infrastructure.

5. Engage Diverse Stakeholders

Regulations often emphasize the importance of diversity and inclusion. This is because many countries are striving to become “AI Hubs.” Through collaberation and integration with professionals, this goal can be achieved much quicker. 

  • Collaborate with domain experts, ethicists, and legal advisors to review your system.
  • Incorporate feedback from diverse user groups to address potential biases early.

6. Monitor and Mitigate Algorithmic Bias

Algorithmic bias can lead to discriminatory outcomes and regulatory penalties.

  • Implement bias detection tools during development.
  • Continuously evaluate model performance across different demographic groups.
  • Use synthetic data to balance datasets where real-world data is incomplete or skewed.

7. Implement Robust Documentation Practices

Good documentation is essential for demonstrating compliance. Relevant authorities can ask for documentation at any point throughout your AI systems life-cycle.  Therefore, maintain:

  • A model card detailing the intended use, limitations, and performance metrics.
  • A data sheet summarizing dataset origins, preparation steps, and ethical considerations.
  • An ongoing log of updates made to the AI system post-deployment.

8. Conduct Regular Audits

Periodic reviews ensure your AI system stays compliant as regulations evolve.

  • Use independent auditors to review system compliance objectively.
  • Include technical, ethical, and operational aspects in your audits.
  • Address audit findings promptly with corrective actions.

9. Train Your Team on AI Ethics and Compliance

A well-informed team is your first line of defense against non-compliance.

  • Offer regular training sessions on relevant AI regulations and ethical guidelines.
  • Use practical examples to illustrate potential compliance pitfalls.
  • Foster a culture where team members proactively raise ethical concerns.

10. Establish a Governance Framework

Governance frameworks provide structure and accountability.

  • Create a Responsible AI Committee to oversee compliance efforts.
  • Develop standard operating procedures (SOPs) for deploying and maintaining AI systems.
  • Regularly review and update your governance policies as technology and regulations evolve.

Conclusion

Meeting regulatory standards for AI systems is not just a legal obligation but a competitive advantage. By following these ten steps, your organization can build trust with stakeholders, avoid costly penalties, and contribute to the ethical evolution of AI.

I must stress that this article, is a very straightforward and simple way of explaining AI compliance in its most basic form. For further guidance or support, feel free to contact our Global AI Law team.