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AI & Data Best Practices

Top 10 Ethical Considerations for AI Projects

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Artificial Intelligence is seen by many as a great transformative technology. However, as with any transformative technology the impacts on society can be tremendous.

As such, if your organization is going to be using AI and ML technologies, it makes sense to spend time considering what you want these systems to do and make sure to address ethical considerations now so that you build these systems with the common good of humanity in mind

But what are the top 10 ethical considerations for AI Projects?

In the context of technology, the concept of ethics applies to how technology is used by humans to accomplish goals. And also the way in which technology systems operate in relation to humanity. Ethics primarily relates to human-to-human, human-to-machine, and machine-to-human interactions. It can even apply to machine-to-machine interactions in which there is some human component, impact, or aspect.

Here are ten considerations you should take into account when building and using AI systems.

Fairness and bias

One of the most important ethical considerations for AI is ensuring that the technology is fair and unbiased. This means taking steps to prevent discrimination based on factors such as race, gender, and socioeconomic status. It also means paying attention to data the system is trained on.


Another key ethical consideration is transparency. This means being upfront about how AI systems work. It also means providing users as much visibility into overall system behavior as possible. Additionally, it means making sure that users understand how their data is being used and protected including addressing appropriate disclosure and user consent.


Privacy is a critical consideration for ethical AI. This means taking steps to protect user data and ensure that it is not misused or mishandled.


Ensuring the safety of users is another important ethical consideration for AI. This means taking steps to prevent accidents or harm caused by AI systems. It also means safety and respect of the environment. And not use resources to the extent that it becomes a significant net negative impact on the environment.


Explainability is an important ethical consideration for AI. This means making sure that users understand how AI systems make decisions. And, providing users with explanations when requested. In the case that AI systems can’t use fully explainable AI algorithms, AI systems should provide a means to interpret AI results so that cause and effect can be understood.

Human oversight

Another ethical consideration for AI is the need for human oversight. This means having humans in the loop. These humans can ensure that AI systems are behaving as expected and are making decisions that align with human values. Also following any laws, regulations, or company policies.


Trustworthiness is also important. This means taking steps to build trust with users by being transparent about how AI systems work. And, being accountable for any errors or problems.

Human-centered design 

Human-centered design is an ethical consideration for AI. This means designing AI systems with the needs and wants of users in mind, rather than just focusing on technical capabilities.


Responsibility is also important. This means taking responsibility for the actions of AI systems. And, being accountable for any negative impacts they may have.

Consider the Long-term Impact of AI

Long-term impacts are an ethical consideration for AI as well. This means considering the long-term effects of AI systems. Not just on society but also the planet. And then taking steps to mitigate any negative impacts.

AI is a powerful technology that has the potential to greatly benefit society. But, it’s also critically important to consider the ethical implications of its use. By taking steps to ensure fairness, transparency, privacy, safety, explainability, human oversight, trustworthiness, and long-term impacts, AI can be intentionally created to align with human values and follow ethical standards.

Dig even deeper into ethical AI considerations

Listen to our AI Today Ethical & Responsible AI podcast series for even more insights. These episodes dig into the specific areas of making AI more trustworthy, ethical, and responsible.

If you’re going to be running or managing an AI Project, enroll in our CPMAI+E training and certification. Get the certification endorsement you need for AI Project success.

CPMAI+E is the most comprehensive training and certification. It provides a step by step approach for running and managing AI projects incorporating a Trustworthy AI Framework. This ensures you’re keeping your AI projects ethical, responsible ,and trustworthy!

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Dive even deeper by getting trained and certified with Cognilytica’s Trustworthy AI Framework Training & Certification.

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