How To Ensure AI Treats Every Customer Fairly

Every day, more organizations are leveraging AI to resolve customer issues without agents, and more callers are getting used to natural, human-like support from machines. 

As AI assumes more significant roles in these contact centers, ensuring fairness and equity in its application is paramount.

AI systems, while incredibly powerful, can inadvertently perpetuate biases, provide misinformation, or treat customers unfairly if not carefully monitored and regulated. 

“These technologies are being built without the kind of regulation that we might see in other technologies,” says Elizabeth Adams, Stanford Fellow and responsible AI leader. “So we’re asking the companies themselves to build in these safeguards. And many of them are not.”

This is especially concerning for contact centers, where customer interactions are diverse and sensitive. 

For organizations evaluating AI solutions, or building a solution in-house, ensuring AI treats every customer fairly is not just a matter of ethical responsibility but also a crucial business imperative.

Ensure fairness from the start of data collection

One of the primary challenges in AI fairness within contact centers lies in data bias. AI algorithms learn from historical data, including past interactions, customer profiles, and feedback. If this data is biased, reflecting societal prejudices or systemic inequalities, the AI model will inevitably replicate and potentially amplify these biases in its decision-making processes.

To mitigate this risk, contact centers must adopt robust data collection and preprocessing techniques. This involves identifying and rectifying biases within the training data, employing diverse datasets that represent all customer demographics, and regularly auditing algorithms for fairness.

“No matter what you want your AI to do, where are you getting your data from? Who’s annotating it? Are you procuring? Does the source that you’re procuring it from have responsible data principles? How is it being aggregated? All of these things happen before the life of the algorithm takes place.”

Embed guardrails into the platform itself

In the case of generative AI, the data source it’s trained on is essentially the entire internet. That means there are an infinite number of ways it can go off the rails or hallucinate if given certain prompts by a user. 

In the past week alone, two such instances have forced companies to abruptly rethink their AI strategies. An AI customer service chatbot for international delivery service DPD used profanity with a customer, “wrote poetry about how useless it was” and criticized the company as the “worst delivery firm in the world.

Elsewhere, Canada’s Civil Resolution Tribunal ruled that Air Canada must fulfill a reimbursement to a customer that was erroneously promised by the airline’s AI chatbot. According to the Tribunal, it is incumbent upon companies “to take reasonable care to ensure their representations are accurate and not misleading” and that Air Canada failed to do so.

While it may be disheartening to hear stories like this, it shouldn’t discourage contact centers from pursuing AI – as long as they do it responsibly. Even if your training data is procured by a third party LLM, there are many measures you can take to ensure hallucinations can’t happen. For every story of AI-gone-wrong, there are plenty of successful implementations that have led to millions of dollars in cost savings, higher customer satisfaction and increased agent retention. 

Understand the role of machines versus humans 

Recent advancements in AI have made machines more natural-sounding than ever before. But no amount of human-ness negates the fact that machines are designed to simply automate a previously manual task. While AI should speak and interact with customers as naturally as possible, it shouldn’t attempt to subvert callers to prevent them from escalating to an agent if they wish to, or if their request is urgent.

“The first thing is transparency and making sure the person knows that they’re speaking with a bot, ” says Adams. “There should be a clear distinction, so that I immediately know that I’m not talking to a human.” 

Across millions of interactions at some of the world’s most well-known enterprises, every Replicant call begins the same way: “Hello, I’m a Thinking Machine on a recorded line. How can I help you today?” This, of course, is by design. 

Replicant was built on the idea that humans are ready to have productive conversations with machines. Success is measured by the machine’s ability to resolve customer issues. Not its ability to make callers believe they’re speaking to a human. Not by attempting to humanize the Thinking Machine with fake typing noises or a human name. And not by preventing callers from speaking to an agent when they want to. 

Promote a culture of diversity and inclusion

Regular monitoring and auditing of AI systems are integral to ensuring ongoing fairness. Contact centers should establish metrics and benchmarks to assess the performance of AI in real-world scenarios continually. This includes evaluating outcomes across different demographic groups, detecting and addressing any disparities promptly.

“My research is all about inclusion in the design and development, so that your output is representative of what you want and your customers want,” says Adams. “Whether you’re talking about algorithms to determine mortgages or facial recognition technology or bots in customer service, involving the people who are most likely to be impacted by your technology should basically be 101.”

For contact centers, that means fostering a culture of inclusion. By embracing diverse perspectives among employees – for example, including experienced agents in the conversation design process of AI – contact centers can better identify and mitigate biases at every stage of the AI lifecycle, from data collection to model deployment.

Partner with a proven solution like Replicant

You may be wondering how contact centers can possibly be responsible for data governance, ethical conversation design, AI guardrails and diversity in thought on top of their day-to-day responsibilities. 

Fortunately, they don’t need to be experts. Enterprises partner with Replicant for our proven track record of reliability and performance, having been tested and refined over time to meet the specific needs of contact centers. This reliability translates into more consistent outcomes through our unique resolution-based approach. 

Additionally, partnering with an AI solution provider allows contact centers to leverage specialized expertise and support, including ongoing maintenance, updates, and troubleshooting. This ensures that the AI system remains up-to-date and effective in addressing evolving customer needs in a fair manner.  With minimal IT requirements, Replicant offers customization options to tailor our platform to the unique requirements of each contact center, enhancing flexibility and scalability. 

Talk to an expert to learn more about how Replicant can resolve your most common calls, lower costs, and drive business growth while ensuring every customer is treated fairly.