Recently, we sat down with John Gilroy with Federal Tech Talk to talk about an important piece of AI—XAI. Explainable AI, or “XAI,” is focused on increasing transparency in artificial intelligence. It is building an AI model that is not a complex black box and can be easily interpreted by humans, ensuring we understand how machines make decisions. Understanding how and why machine learning (ML) models make certain decisions allows for a deeper trust in artificial intelligence and better AI solutions.
For companies and government agencies, following Explainable AI Principles can have millions of dollars of impact, mitigate risk, and meet regulatory compliance.
Explainable AI Principles
Before you start an AI project, you need to be asking questions about the potential impact of the finished product. Start by assessing any potential risks or negative outcomes, recognize possible input biases, and make sure the decision-making process is transparent.
In 2020, The National Institute of Standards and Technology (NIST) proposed a starting point for AI ethics in its “Four Principles for Explainable Artificial Intelligence (XAI)” framework. It is recommended that every organization’s AI must:
- Be explainable
- Be meaningful
- Have explanation accuracy
- Have knowledge limits
Your AI model should be able to explain how it reached its conclusions in ways users can understand. This includes people with and without technical knowledge—It must correctly reflect the system’s process, so the system only operates in the conditions for which it was designed. And just as importantly, it should fail gracefully when asked to perform a task it wasn’t designed for.
Having a human in the loop allows for more transparency, so a machine doesn’t make an arbitrary decision or manual override without any oversight. Human-in-the-loop models leverage both human and machine intelligence to develop models. The formula of human + computer performs better, with AI augmenting what we already do well.
Incorporating AI Ethics Guidelines helps to ensure the solutions we build and deliver for our clients are following responsible practices, including XAI.
Potential Regulation for AI
Efforts by the European Union (EU) are formally defining what “trustworthy AI” is with transparency defined as one of seven key requirements. The EU led the way with GDPR and is now determining the same level of protection for its citizens with an AI Act. It is our belief that it is only a matter of time before the U.S. and other leading nations adopt similar regulations. Smart leaders will adopt XAI into their AI programs anticipating future regulations.
“Adopting voluntary ethics standards for responsible and ethical artificial intelligence solutions is the first step an organization can take for risk mitigation and mission achievement.” – Claire Walsh, VP Engineering
The Benefits of XAI
Early XAI adoption can help you achieve objectives and maximize your business’s potential. It is an optimistic and healthy way to look at the future of AI rather than seeing it as a burden or an additional investment. An XAI solution can offer these benefits:
- Reduce the impact of model bias – By explaining decision-making criteria systems can reduce unintended outcomes and reduce bias by monitoring the model
- Build user trust and faster adoption– As users understand why and how models make decisions, they will adopt AI more quickly
- Manage risk and meet compliance – Reduce the risk of unintended outcomes and meet privacy and industry standards and potential regulations
- Derive actionable insights – XAI encourages humans to understand how and why an algorithm determines its output for more impactful insights
“With XAI, we can gain the trust needed to make AI feel like less of a threat and see it as a helpful way to reduce tedious manual labor and improve lives.” Henry Jia, Data Scientist
Where to Start with XAI?
Any AI project needs to consider XAI. If you’re looking to begin an AI project, it’s most efficient to engage with a company that knows the landscape. They’ll need vast expertise and an understanding of the advancements being made in the field. An expert can guide you through what makes the most sense to your organization. Explainability and transparency should be incorporated into your MLOps approach when building machine learning (ML) solutions.
For more information about XAI, considerations, and a real-life use case, download “An Introduction to XAI” eBook: