Applause Re-Invents AI Testing With New Solution That Detects Bias

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According to a new press release, “Applause, the worldwide leader in digital quality and crowdsourced testing, today announced its new solution for AI training and testing. The scalable solution trains algorithms to learn quickly and tests the output to ensure those algorithms are processing and responding appropriately. The solution leverages Applause’s global community of vetted testers to deliver the widest possible range of training inputs. The results are then tested across every possible device, location and circumstance to identify issues and provide actionable user feedback in real time. This enables today’s leading brands to identify issues of quality or bias earlier in the development process so that they are ultimately delivering top-quality AI experiences for their customers. ‘Users want AI to be more natural, more human. Applause’s crowdsourced approach delivers what AI has been missing: a diverse and large collection of real human interactions prior to release,’ said Kristin Simonini, VP of Product at Applause. ‘Not only will this improve AI experiences for consumers everywhere, the breadth of the community also has the potential to mitigate bias concerns and make AI more representative of the real world’.”

The release continues, “The data Applause collects from the community comes from people across numerous countries, ages, genders, races, cultures, political affiliations, ideologies, socioeconomic and education levels, and more. This broad base of data samples results in a more representative and unbiased output than if the data were sourced from a smaller group. Not only does the Applause Community provide diverse training data sets to power algorithms – it can also test the outputs of those algorithms to check for bias. If bias has crept into an algorithm at any stage, the community can identify it when testing the output, something that a smaller or less diverse group of testers might not be able to do.”


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