What Is Explainable AI & Why Is It Important in Marketing?

As AI technologies continue to expand, they profoundly impact our lives. It is changing how we buy, whom we hire, our friends, what newsfeed we receive, and even how our children and elderly are cared for. It is used in various marketing applications such as personalising product and content recommendations and optimising cost-per-click and cost-per-acquisition in ad targeting by mining troves of online consumer behaviour data.

With the rise of artificial intelligence (AI), it’s no surprise marketers are eager to harness the power of machine learning for their businesses. AI is employed in various marketing applications, such as personalising product and content recommendations and optimising cost-per-click and cost-per-acquisition in ad targeting by mining online consumer behaviour data. Frontier applications predict individuals’ future needs and recommend actions to them.

In this article, we'll explore what XAI is—and how you can use it to build more trustworthy AI systems for your company or product.

In the past, AI systems have been largely incomprehensible. Their decision-making processes were opaque and hidden away from users, often leading to concerns about bias and unfairness that could not be addressed.

To address these issues, explainable AI (XAI) is the class of systems that provide visibility into how an AI system makes decisions and predictions and executes its actions. XAI explains the rationale for the decision-making process and surfaces the strengths and weaknesses of the process, providing a sense of how the system will behave in the future.

AI is employed in various marketing applications, such as personalising product and content recommendations and optimising cost-per-click and cost-per-acquisition in ad targeting by mining online consumer behaviour data. Frontier applications predict individuals’ future needs and recommend actions to them.

AI can be used to understand customer behaviour in unprecedented detail. This helps companies make more informed decisions about what products to offer customers, how to market them effectively, and when it's time for an intervention. For example, AI can recommend specific products based on the customer's purchases or browsing history. It can also be used for predictive purposes: for example, by predicting what customers will buy next based on their browsing habits and past purchases.

Personalisation has been an essential feature in e-commerce marketing for decades, but personalisation has become more sophisticated and pervasive with the advancement of AI capabilities. For example, search engines have begun incorporating AI into their search algorithms, making it possible for users to receive more relevant search results by learning patterns from previous searches. Other examples include Facebook’s “People You May Know” section, which suggests friends based on mutual connections or interests; Amazon’s “Frequently bought together” feature, which means additional products based on what other customers purchased with a specific item; Netflix recommending TV shows based on past viewing behaviour; TripAdvisor offering hotels based on past reviews; Yelp suggesting restaurants based on previous ratings.

Explainable AI (XAI) is the class of systems that provide visibility into how an AI system makes decisions and predictions and executes its actions. XAI explains the rationale for the decision-making process, surfaces the strengths and weaknesses of the process, and provides a sense of how the system will behave in the future.

The term "explainable" refers to a system that can explain its outputs to humans. The term "AI" refers to artificial intelligence, which is any technology capable of performing tasks indistinguishable from humans.

Why is this important? Imagine you’re serving as a juror in a criminal trial where an expert witness has testified that a suspect’s fingerprint was found on the victim’s door handle at the crime scene. You want to know if it would be reasonable for this expert witness to testify about this fingerprint evidence because it could help you identify or rule out suspects. You also want to know how reliable this fingerprint evidence is concerning other evidence—what are its strengths and weaknesses?

Explainable AI (XAI) is a promising new type of artificial intelligence. It's designed to give businesses and their customers a better understanding of how AI systems work, makes decisions, and why they make them.

This makes it easier for AI-powered products to be used in business settings, where the user may not be a data scientist but still wants to know how an AI system will behave. Because XAI systems are more transparent, they'll be more trustworthy, which could help businesses use them in hiring decisions or customer service—even when using machine learning.

Explainable AI also raises ethical questions that often crop up when we don’t understand how something makes a decision. How much explanation do we need to feel comfortable with a decision? Who does the explanation help, the explainer or the explained? With all these questions, it’s easy to see why Explainable AI will be an important study area moving forward.