Explaining Explainable AI

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Read more about author Bill Schwaab.

As more industries leverage artificial intelligence to support vertical-specific operations, many without experience in managing a virtual agent may soon do so. Just last year, for example, investment in Insurance technology topped $17 billion. The ongoing transformation of the sector will depend, in part, on the successful implementation and management of artificially intelligent solutions. However, many professionals across digitizing industries do not have a background either academically or professionally that would allow them to easily understand the technical elements of these systems.

For data professionals, a program like a virtual insurance agent can be easily understood. The agent becomes the throughline between a series of stakeholders from firms, to their customers, brokers, lienholders, and claims staff. Those with technical backgrounds who understand incorporating AI into logistically significant tasks like underwriting, claims processing, and customer service must address the nuances of the industry.

However, additional considerations should be made for the wealth of data virtual agents both collect and connect. Businesses launching a technical product want to be able to scale up business and an artificial intelligence program’s capabilities. Optimizing processes after the launch of a project should include fostering an understanding between all parties. 

When an AI trainer or firm leadership is aware of the processes behind a program’s operation, they’re more empowered to leverage insights compiled by AI. Explainable AI removes the traditional “black box” that obscures program operation. The knowledge of not only how but why an AI took a course of action maintains integrity behind these critical tasks by making the technology more approachable. 

In the larger view, explainable solutions aid the valuation of a technical project. IT teams work across implementation and configuration stages to ensure a virtual agent or an AI program works as intended. After a project is launched, and an organization takes a more primary role in management, there are knowledge gaps that can arise. Virtual agents consistently source customer feedback, and their conversation flows and capabilities will need to be updated as they operate. An explainable solution in this instance allows AI trainers or managers to review service chats with information on the course of action that led to a specific outcome.  

Ultimately, technical professionals can and should illustrate the benefits of this kind of data collection and optimization. Contextualizing the foundations of artificial intelligence is key to any partnership on a technical project. However, an additional benefit to project teams is incorporating elements of explainability into a final AI product. Not every member of a project team or partner organization has the same background. For many, artificial intelligence is still a mystifying subject. 

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