Demand for conversational AI is on the rise. Generative bots such as ChatGPT have taken the world by storm, showing companies just how effective the right algorithms can be at responding to, supporting, and communicating with end-users.
At the same time, improvements in natural language processing and understanding technologies are leading to the creation of ever-more human bots. Today’s tools can hold conversations with us, express personality, and even respond creatively to prompts, using machine learning. Unfortunately, just like human beings, bots aren’t without flaws.
One of the most common issues companies face when producing their own bots is how to avoid the ethical problem of bias. While bots may not be subject to unconscious bias in the same way as us humans, they can still portray biased behavior based on the data they’re given.
Failure to pinpoint and remove bias from a bot not only damages the end-user experience but can also lead to inaccuracies in data and harm brand reputations.
So, what can businesses do about conversational AI bias?
What Is Conversational AI Bias?
For the time being, chatbots and virtual assistants don’t have opinions and emotions of their own.
This means they can’t really be subject to emotional, unconscious bias. Voice bots don’t deliberately ignore statements made by customers with a strong accent because of racist tendencies. However, they can show bias in a multitude of different ways because of their training.
Just like people have unconscious biases which affect how they behave and communicate with others, conversational bots can have biases that damage the quality of their interactions.
In fact, there are some relatively shocking examples of this throughout the world. Several years ago, Microsoft even made headlines with a Twitter chatbot (Tay) that unintentionally collected too much data from hate speech and seemed to become a racist, sexist entity overnight.
The reason bots develop biases is usually poor training and testing. Bots can only learn and respond to comments based on the information they’re given. If the data sets of a bot are limited or unintentionally biased, the bot itself will be biased as a result.
How to Reduce Conversational AI Bias
In recent years, major public issues with conversational AI bias have drawn attention to just how significant the problem can be. As a result, companies have become more cautious with the way they create, train, and test bots before rolling them out for public consumption.
Ultimately, reducing or eliminating conversational AI bias is just a matter of making sure bots are trained and deployed as ethically as possible.
Step 1: Collect Better Data
Data is the lifeblood of any conversational bot. Every word a bot says, or types to a customer is a byproduct of the data it has accessed in the past. Bots don’t just come up with answers to questions on their own, they scan through countless data points to find relevant responses.
To avoid bias in those responses, companies need to ensure they’re providing their bots with access to the right, holistic data. Collecting larger amounts of data, from multiple viewpoints, perspectives, and environments, can allow companies to create a more diverse, bias-free chatbot.
Step 2: Analyze the Bot’s Ability to Understand
Chatbots are reliant on a number of AI algorithms to function. The most advanced bots on the market today leverage a combination of natural language processing, and natural language understanding (NLU) tools. Without the right “NLU” strategy, these bots can only collect data, but they can’t really pinpoint what customers mean when they say certain things or what their intent might be.
Analyzing a bot’s ability to understand information using rich analytics ensures companies can transparently track how their bots are processing data. Evaluating the NLU process can help organizations to immediately pinpoint flaws in the NLU workflow, which may lead to bias.
