Ensuring a Diversity of Data in AI-Led Self-Service

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“Diversity” is a common theme today, and it often manifests in public and private organizations seeking to bring a wide variety of faces and voices to its problem-solving and management tables. It’s a worthy goal: governments, medical research, institutes of education and private organizations can all benefit from broadening the experience to encompass the perspective of all humans. Data collected from homogenous sources will very probably yield homogenous results that don’t apply to everyone (for example, the years of medical research conducted only on male test subjects, which left a gap in understanding how disease manifests in women).

When it comes to enterprise data, diversity is also a good thing. Companies use a great deal of data today to make predictions, build schedules, manage the workforce and boost the quality of customer service they offer. But they, too, need to be careful to include a little diversity in this data juggling, or they’ll limit the utility of the products they’re building, according to a recent blog post by Aspect’s Lisa Michaud, who titled her post “Data Doesn’t Lie, But It Doesn’t Tell the Whole Truth” and described the mistakes that can be carried into machine learning technology.

“From my personal area of science, gathering diverse textual linguistic data is just as challenging,” she wrote. “It is a key step to creating a chatbot or virtual assistant, but a single developer entering sentences into a chatbot toolkit will create a bot who can answer questions posed by that developer – but not questions from someone who expresses herself in very different ways.”

Read the source article at Contact Center Solutions.