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Aug 26, 2021

Fuzzy Logic Makes AI Explainable

Despite its growth and popularity, AI is not as widely adopted as it could be—particularly by small- and mid-size businesses that lack the enormous data sets of business and tech giants. One reason is that AI designers are not doing enough to consider the end user.

Consider the many stages an AI solution must pass through in order to be successfully integrated into a company’s workflow. An algorithm begins with an AI expert who must incorporate knowledge from a subject matter expert. Then the tool needs approval from leadership, and has to be adapted by users. Too often, our algorithms do not reflect the complexity each stakeholder brings to the table. Every one of those steps may require the solution to meet a different need.

Take for instance an algorithm used for hiring. The subject matter expert who knows what job criteria matters might need to provide relevant data for the AI, like the neural network, to learn from. Then the HR and executive leaders who must approve the tool may want to know how the algorithm combats bias, whether it can help them find non-obvious candidates, and even how to compare similarly ranked candidates. (For example, potential bias might be reduced by ensuring an applicant’s years of experience “count” more than the name of their college.) Meanwhile, end users will care about ease of use and whether they get what they’re looking for when they use it.

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