Every algorithm is only as good as its data. And that data must be continually reality-tested and updated so the algorithm hones in on the true, precise, and productive results you want.
AI algorithms, however, exhibit a special challenge: They operate opaquely. This lack of transparency means even the data scientists who design them can’t always tell you exactly why an AI agent makes a particular decision or recommendation.
Knowing why your algorithms are behaving a certain way isn’t just nice to know. In fact, how well you understand how an AI algorithm actually works impacts how well it meets your needs and goals. Unless you can tell why the AI agent did what it did, you might not realize right away that the results are off, let alone know what adjustments you should make to correct for it.
But: Answering “Why?” is about much more than just debugging a flawed process. More important, knowing why gives you actionable insights for addressing sticky problems in new ways, insights that might lead you to tackle issues you hadn’t previously considered or even imagined.
Answering “Why?” is also about trusting in your AI, so you’re confident of its ability not just to produce accurate results, but fair, transparent ones. This becomes especially critical when you consider that AI systems may incorporate, and further entrench, the (unconscious) biases of their developers, as well as other faulty premises.
So: What is Explainable AI—and why is it essential to the success of our industry-leading conversational AI technology and tools?
Take a look at the bot examples, below.
Text: my router isn't working
Predicted label: internet-get_help
True label: internet-get_help
This example affirms the bot’s accuracy. Based on the input, “my router isn’t working,” the AI selected "router" and "isn't working" as the most important words and predicted the label as “internet-get_help.” The AI arrived at the same, “true” result as did the live agent.
Text: Please note that I did not receive your bill while you are charging late fee
Predicted label: fee-waive_charge
True label: billing-issue
Here, the bot’s predicted label (fee-waive_charge) and the true label (billing-issue) don’t match. The AI chose “bill” and “late fee” as the two most important words but ignored “did not receive.” The customer is not seeking a fee waiver (at least, not at this point) but, rather, has a billing issue (didn’t receive one), which happened to trigger a late fee.
Knowing why the AI selected “fee-waive_charge” makes all the difference.
In data science, there’s always a lot of data wrangling; optimizing these models, and analyzing the results, is a complex process. Explainable AI helps us solve it.
But the Explainable AI tool has relevance beyond data sciences. For example, various groups, such as professional services, can use Explainable AI to understand why they're getting the responses they're getting and then make instant improvements.
Even though no model is or will ever be perfect, Explainable AI is extraordinarily helpful at every step along the continuum toward 100 percent accuracy—optimizing algorithms via more targeted improvements on your dataset. It enables our clients to see why their models are behaving a certain way and enables us to see how to make them work better.
And of course, a successful AI operation requires an entire, integrated ecosystem of prediction tools and models; Explainable AI is just one piece of the puzzle.
Put another way, the answer to, “What is Explainable AI?” is: The foundation to good AI practice.
Explainable AI is a hot topic. But not every AI-powered customer engagement platform is using it—and no one in the CX industry is as far along with Explainable AI technology as we are.
Want to get all the benefits of Explainable AI in a customer engagement platform? You need to be using 7.ai Engagement Cloud™.
To learn more about what’s under our AI hood, visit the Technology web page: 7 AIVA—Conversational AI Chatbot Technology with NLP.