Confronting Bias in AI-Driven CX

Alongside the promises of AI-driven CX comes the risk of bias, which can inadvertently perpetuate inequalities and erode trust. Gartner estimates that an incredible 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or because of the people tasked with managing them.

A team of researchers from the USC Information Sciences Institute (ISI) studied two AI databases to see if their data was fair. They discovered that up to 38.6% of the data was biased. Even more alarming is that they found the algorithms were outputting even more biased information than they were given.

“It was alarming to see that this biased data tends to be amplified, because the algorithm is trying to think like us and predict the intent behind the thought,” said Pei Zhou , a USC Viterbi Ph.D. candidate and research participant. “We are often concerned about our own data and say it’s not too bad and we can control it. But sometimes the bias is amplified downstream, and it is outside of our control.”

Jay Pujara , a USC Viterbi research assistant professor of computer science, added, “It was disappointing to see that a little bit of bias can strongly affect predictive models. AI’s entire reason for existing is that they identify patterns and use them to make predictions. Sometimes it’s very helpful - like they see a change in atmospheric pressure and predict a tornado - but sometimes those predictions are prejudiced: they decide who to hire for the next job and overgeneralize from prejudice that is already in society.”

The deployment of AI in CX brings forth the challenge of mitigating these biases, ensuring fair and ethical treatment of customers, and fostering trust in AI systems. Read on to discover best practices for identifying, mitigating, and ultimately preventing bias, thereby paving the way for fair and equitable AI implementations: