Writing a blog post to explain something called ‘Explainable AI’ seems a bit strange, right? Well, it’s actually one of the most important elements of today’s artificial intelligence-filled world and one that you should definitely take the time to read through and comprehend. Why? Because explainable AI is a branch of artificial intelligence that sets out to ensure that AI is understandable to all and that users know how it works, how it was trained, and how its results are created.
You’ve probably heard some wariness around AI tools: how can we know who’s behind it? Can we trust its results? What if something goes wrong? These are all valid questions and ones that you’ve definitely heard as AI gains more popularity. And luckily for you (and everyone else!), the reach of explainable AI is expanding and more people are recognizing its importance.
In this article, we’ll first explore the need for explainable AI (XAI), its benefits, and how it can be applied in real-world situations to make AI more accessible for all.
The Need for Explainable AI
We have machines and tools that can bring us results in a matter of seconds, completely transforming the way we work. But as we put more trust in these tools, we can’t lose sight of the importance of understanding exactly how they work and how they’re arriving at specific decisions. Users need to understand how decisions are made, why a specific decision wasn’t made, and what makes an AI model work or fail.
Why? Because of the three main pillars of explainable AI:
Interpretability: the outputs of AI systems need to be understandable for users and this means that the decision making process was clear.
Transparency: when decisions are made, the training, decision-making process, and possible outcomes need to be completely understandable.
Trustworthiness: AI models are useless if their results aren’t trusted by users and this trust is gained through a clear understanding of how decisions are made.
There are three main techniques used to ensure that AI systems are explainable:
Prediction accuracy: for AI tools to be useful, they must be accurate. And with prediction accuracy which looks at the outcomes from training data, we can have a solid guess of how they will react when actually put to the test in real world situations, which helps users trust more in the AI system.
Traceability: the decision making process of a tool should be clear to users and with a clear trace of the steps a machine goes through to make a decision (and the limitation it has when making said decision), the user will have more confidence in the AI tool.
Decision understanding: not all the pillars of XAI are linked to the AI model directly; in fact, companies that employ AI tools must educate their employees on how AI tools work to guarantee the highest possible degree of decision understanding.
The Benefits of Explainable AI
While the use of AI tools is certainly beneficial for companies in a wide range of industries, there are specific advantages for those that choose to prioritize explainable AI.
Increasing trust in what AI products can do
We know that AI tools have the potential to revolutionize practically every industry and that’s why ensuring everyone is on board and trusts AI models is key. As more and more of your employees embrace AI technology, productivity and results will grow, helping you become more profitable and effective. If, however, your employees aren’t sure about AI technology, where it comes from, or if it can be trusted, your company could quickly fall behind.
Seeing better results from AI products
When you actually understand how an AI system works, you’ll be better suited to optimize it and make changes to make the most out of the tool. If everything is a mystery, however, you could be missing out on incredible results that are only seen with a strong comprehension of the actual process.
Adhering to local and international regulations
As AI tools become more widespread, more and more regulations will be put into place that control how they can be used, in what cases, and when. Therefore, a strong understanding of how the AI tool works will help you ensure you’re meeting all regulations and avoid problems or fines down the road.
Committing to the ethical use of AI
In addition to the rise of guidelines that regulate AI usage, the question of ethics in AI has also become a hot topic recently and one of the main pillars of the ethical use of AI is transparency, which is achieved through explainable AI. In addition, diving into the ethics of AI can help identify bias and make the AI tools more effective–and legal.
Committing to Explainable AI
If we’ve convinced you of the importance of explainable AI, that’s great: it’s time to discuss how to commit to explainable AI practices in your company, ensuring that interpretability, transparency, and trustworthiness are always at the front of mind. And while the exact steps that your company needs to take will depend on your sector and team’s experience with AI, these are some great tips to nudge you in the right direction:
Create an explainable AI team: in order to properly employ explainable AI practices at your company, you’ll need to delegate this responsibility to a team of AI professionals who are up-to-date with both regulations and AI developments to guide the implementation of explainable AI at your company. By including professionals with a wide range of backgrounds and experiences with AI, companies can ensure they’re using AI models that are truly explainable.
Train your employees about AI: apart from the team that’s tasked with ensuring all your AI models are explainable, you’ll have to ensure that all employees know how to work with AI systems, what they are capable of, and how to make the most of them. For some, this will be quite the undertaking as they will have had very limited exposure to AI, but it’s required for any company looking to truly commit to explainable AI.
Stay up to date with AI: as a new and rapidly expanding field, AI is changing constantly and knowing how AI systems work means you’ll need to commit to staying up to date with changing technologies so that you don’t fall behind–and fully understand how even the latest development works.
Ready to take on the AI world? Explainable AI starts with a deep understanding of AI principles and at Ironhack, you can gain exactly that through or AI Engineering Bootcamp, Data Science & Machine Learning Bootcamp, or any of our AI School short courses.
Don’t waste another second and start your AI journey today.
