How To Develop An Effective AI Policy

As artificial intelligence reshapes industries worldwide, it’s imperative for every organization to craft an AI policy that not only addresses today’s challenges but also anticipates tomorrow’s opportunities. In a recent article, I covered the vital need for every business, no matter the industry it’s in, to have an AI policy in place.

A comprehensive guide on how AI should be used—or not used—is critical for organizations that want to avoid risks such as breaching privacy, exposing sensitive data, infringing copyright, and ultimately destroying customer trust.

Or, to put a more positive spin on it, clearly setting out guidelines for acceptable AI use would position a company to capitalize on what is set to be the most transformative business opportunity of our lifetimes.

The next question is how to create this policy. Here, I’ll provide a step-by-step guide to ensure all the bases are covered.

1. Identify And Include All Stakeholders

First, who is going to create your policy? To me, it makes sense to involve everyone who will be affected by AI. This will establish buy-in across the organization and avoid “top-down” policy implementation that can leave those who are most affected feeling alienated. Form a working group consisting of representatives of every group whose views need to be included, including executives, technical experts, legal experts, minority groups and frontline staff.

2. Explain What The Policy Aims To Achieve

Clearly set out the intention behind the creation of the AI policy. Communicate the risks of unregulated AI use and the benefits of building a culture around responsible, ethical and accountable use of AI. This is about education and is where awareness can be raised of dangers such as bias, discrimination, breach of privacy and exposure of confidential information.

3. Establish Accountability

Who will be responsible for ensuring that AI is used in a way that’s safe, ethical and fair? Ensure everyone using AI understands that they’re responsible for its outcomes, and particularly, they have an important part to play in mitigating its risks. Ensure a reporting process is in place for anyone concerned about any aspect of AI use and that everyone knows how to access it.

4. Audit Current AI Use

This involves compiling a list of all the current use cases for AI within your organization, big or small—from employees using ChatGPT to draft emails to more sophisticated operational use cases such as predictive analytics or personalized marketing initiatives. Make sure you’re aware of every use that’s being found for AI, who is using it, what data and tools are involved and what it hopes to accomplish. It’s a good idea to assign a risk level to each use case based on how likely it is to cause harm due to the risks we’ve identified. This will be useful when it comes to defining rules and guidelines, as described below.

5. Evaluate Compliance And Regulatory Obligations

Next, conduct a thorough assessment of the laws and regulations that you’re obliged to follow. This will vary depending on your jurisdiction, the industries you’re involved in, and the tools, use cases and data you’re using. This knowledge will be critical when it comes to creating your own internal rules and guidelines around AI use.

6. Onboarding New AI Tools And Use Cases

Establish a process for adopting and implementing new AI tools, processes and use cases. Who is responsible for vetting and ensuring they are compliant with your policy, and what safeguards should be in place before they are rolled out into operation?

7. Define Your Guidelines

Now, it’s time to set out some specific rules. Identify individual tools or data that are acceptable or unacceptable based on business requirements, the need to facilitate innovation and regulatory obligations. If, for example, it’s decided that employees should be free to use tools like ChatGPT for low-risk activities like drafting emails but shouldn’t use it for anything involving customer data or confidential business information, this is where this should be stipulated.

This section will perhaps be the most comprehensive part of the policy and will also be used to set out high-level obligations, such as ensuring customers are always made aware when AI is used to make decisions that affect them or mandating fact-checking and verification of any content created with the help of AI before it is published.

8. Education And Engagement

Set out guidelines and best practices to ensure that all employees are both educated about the risks and opportunities of AI and informed about the responsibilities placed on them by the policy.

9. Communicate The Policy

This involves drafting the policy in a way that’s comprehensive but clear and ensuring that it’s accessible to everybody. This could involve creating different versions of a policy document intended for different audiences (new employees, business managers and contractors, for example). Encourage dialogue around the policy and create channels for receiving feedback and suggestions for improvements. Stipulate how the policy will be distributed, where it can be found, and how stakeholders will be kept up-to-date on any changes or updates.

10. Monitoring And Assessing Effectiveness

Finally, you need to know how well your policy is working when it comes to mitigating risks and enabling innovation. Establish some KPIs that could help you track this. Examples might include:

• Compliance Rate – The percentage of AI projects that pass an audit based on the rules set out in the policy.
• Employee Awareness – Results of surveys designed to monitor the extent to which employees are aware of organizational AI policies.
• Incident Rate – The number of reported incidents of AI-related issues such as data or ethical breaches.
• Employee/Customer Engagement Rates – The number of employees or customers using your organizational AI tools, products or services
• External Auditing Performance – Scores or ratings of AI safety, ethicality or accountability based on independent third-party assessments of your systems or processes.

Working through these steps should leave you with a solid foundation on which your organization can build to benefit from AI's exciting opportunities while minimizing the serious risks associated with it.

It’s important, however, to remember that we’re in the early days of the AI revolution. Both the technology and the regulation around it are constantly evolving, and new obligations and risks are certain to emerge.

This means your policy should be constantly evolving, too, with processes in place to ensure it’s kept up-to-date and relevant.

I highly recommend doing it now rather than simply adding it to the to-do list or waiting until you feel as if you have more AI to manage. Implementing it in an early stage of your AI journey means that your AI strategy and infrastructure will evolve on a sound footing.