Business owners today can’t pass up on the potential of artificial intelligence (AI). At the same time, as this technology has grown, so has attention around the ethical issues it presents. If you want to make the most of these tools, you need an ethical AI framework.

Ensuring your AI models and their implementation are ethical isn’t easy, but it protects your business and sets you apart from less careful competitors. Here are 10 steps to follow to do just that.

1. Establish a Formal Code of Ethics

The first step in building ethical AI is to create a code of ethics. If your business already has formal ethical guidelines, base your AI-focused version on these. If not, take the opportunity to develop a written code of company policies and philosophies.

While specific ethics codes will vary between organizations, there are a few general subjects to cover. Make sure you address AI’s impact on jobs, data privacy and existing biases and social issues like gender and racial equality.

2. Gather a Diverse AI Team

Next, it’s time to gather your AI development and oversight team. This group must be diverse — potentially even more so than your workforce as a whole. The more genders, ethnicities and backgrounds are represented in your AI team, the better they’ll be able to spot and address a broader range of issues.

Building a diverse task force is crucial in promoting workplace inclusion, and the same concept applies to AI. If you truly want your AI to ethically address and represent all groups, the team in charge of it must be diverse.

3. Ensure Training Data Is Inclusive

Similarly, any data you use to train your AI model must also be diverse and inclusive. AI biases tend to arise from gaps in their training data. Amazon’s infamous abandoned hiring AI, for example, was biased against women because the company trained it mostly on men’s resumes.

If training data sets represent more people groups, these outcomes are less likely. While data diversity isn’t a silver bullet against AI biases, it’s an essential step in the ethical AI process.

4. Emphasize Transparency

A lack of visibility is another common obstacle to ethical AI. When businesses aren’t upfront about how they use AI and related data, it’s difficult to trust the results. Similarly, many AI models lack explainability — it’s unclear how they arrive at their conclusions — making it hard to spot potential biases or other ethical issues.

Transparency starts with thorough documentation across the AI development timeline. You should also prioritize AI explanability, avoiding “black box” AI in favor of more auditable models to promote more effective ethical review. Make all this information easily accessible to employees and consumers, including admitting areas where gaps or oversight may occur.

5. Outline Ethical AI Usage Guidelines

It’s also important to remember that even an ethically developed AI model can be unethical in its application. Consequently, businesses must create formal guidelines for fair AI usage to ensure they don’t cross any boundaries when implementing this technology.

Because AI makes it more likely that some positions will become redundant, these codes must address AI workforce considerations. That may be a policy to never replace human positions with automation or a commitment to upskilling workers to offset this displacement. Guidelines for avoiding AI copyright infringement and data privacy issues are also crucial.

6. Tailor Ethical Frameworks to Your Industry

As you write your AI training and usage guidelines, remember to consider any industry-specific concerns. While some ethical issues like gender or racial equality apply to all sectors, some businesses face unique challenges they must address in their AI framework.

Medical organizations must take data privacy more seriously than others and consider how AI can impact care standards. AI hallucinations are more impactful in the legal sector than others, so this deserves extra attention. Whatever your industry, you must consider how and why AI may cross ethical boundaries to inform more relevant guidelines.

7. Educate Employees

Remember to keep your employees in the loop once your ethical AI guidelines start taking shape. Anyone using AI in their workflow or whose job may change after larger AI rollouts should know the company’s stance on this technology. Failing to address this could lead to internal conflict or employees unknowingly going against the code of ethics.

Informing employees of your ethical stance on AI is also a good way to encourage inclusivity. Some workers may have a unique perspective, so including them in AI ethics discussions can lead to important improvements you may miss otherwise.

8. Incentivize Collaboration

Along those lines, it’s best to go further and encourage employees to speak up about their concerns or ideas about ethical AI. The more perspectives you include in your framework, the more equitable it’ll likely be.

You can promote this collaboration by incentivizing participation. Create a formal program that rewards employees if they suggest something that leads to a measurable improvement in the AI project or its code of ethics.

9. Don’t Overlook Data Security and Privacy

Remember not to overlook the importance of cybersecurity when focusing on AI’s ethical implications, too. While data security and privacy may seem like a separate issue, no AI is truly ethical if it needlessly exposes people’s information to cyber risks.

Scrub AI data of any personal identifiers wherever possible and store it in highly restricted, encrypted databases to ensure data privacy. Follow industry-specific cybersecurity best practices to keep these models and their data safe. In addition to protecting people’s privacy, these steps will prevent data poisoning attacks that may lead to unethical AI models.

10. Review AI Framework Regularly

Finally, as with many things in business, you must adopt a spirit of ongoing improvement. You likely won’t get your ethical AI framework perfect on your first try. Even if you did, new ethical considerations and complications will emerge as this technology grows.

Review your AI model and its implementation plan before using it, then repeat this process regularly. Every few months and every time you expand AI to a new use case, review it to ensure everything still sticks to your code of ethics. Keep in mind that this code may also need adjustment as time goes on.

Ensuring Ethical AI Is Critical for Businesses Today

Ethical AI can be tricky, but it’s essential. You need to capitalize on AI to remain competitive today, but careless AI usage can create more problems than it solves.

These 10 steps will help you create a more ethical AI framework. When you do that, you can enjoy this technology’s benefits while minimizing its most significant downsides.