7 AI Best Practices for Compliance and Transparency

As artificial intelligence (AI) continues to transform industries, businesses must traverse a complex landscape of ethical concerns, regulatory requirements, and consumer trust. While AI offers immense benefits—ranging from enhanced efficiency to personalized customer experiences—it also poses significant risks, such as bias, data privacy violations, and lack of transparency.

To ensure responsible AI implementation, businesses need to focus on compliance with regulations and maintain transparency in their AI operations. Below, we’ll explore some of the best practices for achieving AI compliance and transparency, helping businesses foster trust while complying with evolving regulations.

1. Understand and Comply with AI Regulations

As AI technology evolves, so do the regulatory structures that govern it. Different regions have introduced or are developing laws that address the ethical use of AI, such as:

• The European Union’s AI Act: A world-first legislation that seeks to regulate AI applications based on their risk level, from low-risk to high-risk categories like biometric surveillance and critical infrastructure.
• The General Data Protection Regulation (GDPR): Mainly focused on data privacy, GDPR also impacts AI systems by imposing transparency around automated decision-making and requiring businesses to explain how their algorithms process personal data.
• The U.S. AI Bill of Rights: The United States framework that offers guidelines on ensuring that AI systems are used ethically and protect the rights of individuals.

Businesses need to stay up to date with these regulations to avoid legal penalties. At the time of this writing, AI is rapidly evolving and growing in its use-cases. Businesses and industries are grappling with almost daily changes to AI regulations, while trying to maximize the potential of AI technologies. Partnering with legal experts or compliance officers is crucial to understanding how each regulation applies to your AI technologies.

Best Practice: Regularly audit your AI systems for compliance with local and international laws and regulations and amend your processes as necessary to meet the requirements. Finally, create an internal team dedicated to monitoring AI compliance across all business operations.

2. Adopt Transparent AI Practices

One of the most pressing concerns surrounding AI is the controversial “black box” AI algorithms—where it is unclear how a machine learning model arrives at certain decisions. Transparency is critical for fostering trust among customers, stakeholders, and regulators.

Best Practice: Invest in explainable AI (XAI) techniques. Explainable AI refers to AI systems designed from the ground up, to provide insights into how and why they reach certain conclusions. By implementing XAI, businesses can offer clearer explanations of how algorithms make decisions, ensuring transparency in cases where AI impacts individuals (e.g., credit scoring or hiring), and increasing the trust between customers and the business.

Additionally, provide clear communication about the purpose, limitations, and risks of AI systems when working with customers. Transparency around data usage, decision-making processes, and system limitations allows consumers to feel assured in your AI-driven operations.

3. Address AI Bias and Fairness

AI systems learn from data, and if that data is biased, the AI will produce biased conclusions and results. This can lead to discrimination in hiring, lending, and other essential decision-making processes. Ensuring fairness in AI is not just a regulatory concern—it’s also an ethical imperative that can protect a company’s reputation and prevent legal risks. Currently, this is one of the primary concerns that causes many consumers to not want to interact with AI.

Best Practice: Establish procedures to identify, monitor, and minimize bias throughout the AI development lifecycle. The procedures may include:

• Conducting bias audits on training data and algorithms.
• Regularly testing AI models for unintended discriminatory outcomes.
• Ensuring that diverse teams, with varied perspectives, contribute to AI design and data input, and implementation.
• Leveraging fairness tools, such as IBM’s AI Fairness 360, which help detect and mitigate bias in AI models.

4. Ensure Data Privacy and Security

AI systems rely on extensive amounts of data to function effectively, often requiring sensitive personal information to deliver personalized services. However, improper handling of customer data can lead to privacy breaches and significant legal consequences under federal and international laws.

Best Practice: Implement strong data governance frameworks to ensure that data collected for AI is stored securely and anonymously, when possible, and used only for its intended purposes. Incorporate privacy-by-design principles in the AI development process to minimize data exposure.

Ensure that all customer data is handled in a compliant manner and provide customers with clear options to opt-out of data collection when necessary. Businesses should also encrypt sensitive data and adopt rigorous cybersecurity measures to protect against breaches.

5. Create an AI Ethics Committee

Given the potential for AI systems to significantly affect people’s lives, many businesses are forming AI ethics committees. These committees are responsible for overseeing AI development and deployment, ensuring that the technology aligns with ethical standards and values.

Best Practice: Establish an AI ethics committee made up of internal stakeholders and external specialists in fields such as ethics, law, and technology. This team should:

• Review and approve AI use cases, ensuring they are ethical.
• Regularly monitor the impacts of AI systems on consumers and employees.
• Establish guidelines on what constitutes ethical AI usage in your industry.

The AI ethics committee should also work closely with the compliance team to ensure that all ethical considerations are accounted for and that AI systems operate within both legal and moral boundaries.

6. Maintain Ongoing AI Monitoring and Auditing

AI systems are constantly learning and evolving, which means that even after deployment, businesses must consistently monitor them to ensure they remain compliant and effective. Changes in customer behavior or regulatory updates can render previously compliant AI models non-compliant, so ongoing vigilance is necessary.

Best Practice: Set up a continuous monitoring system to track the performance and behavior of AI systems. Implement tools that flag any abnormalities from expected behavior, such as “data drift” or biased outcomes. Regular audits should be conducted to check for adherence to transparency, fairness, and privacy policies.

Additionally, maintain detailed documentation of all AI systems, from data collection processes to decision-making algorithms. This documentation is essential not only for transparency but also for regulatory reporting and internal reviews.

7. Educate Employees and Customers About AI

Both employees and customers should understand how your business uses AI. Employees, particularly those involved in developing or operating AI systems, should be trained on ethical AI practices, compliance regulations, and responsible data handling. Consumers should be made clearly aware of your organization’s reliance on AI systems, and how their data is being collected and used.

Best Practice: Organize regular training programs on AI ethics, transparency, and compliance for your workforce. Create clear communication materials that explain how AI is used in customer-facing applications and offer customers easy-to-understand resources about how AI impacts their interactions with your business.

Bringing It All Together

As businesses continue to adopt AI, ensuring compliance and transparency is no longer optional—it’s a requirement for maintaining customer trust and avoiding legal penalties. By staying informed of regulatory changes, and adopting transparent AI practices, businesses can implement AI in a responsible, ethical manner. It is best to wait until your organization can fully comprehend and implement these best practices before deploying AI solutions, ensuring a safe and practical AI experience for your employees and customers.

When done the right way, AI can unlock new levels of innovation and efficiency, while also preserving the integrity and confidence that customers expect. Utilize these best practices to ensure your AI operations are not only compliant but also transparent and fair.