Researchers have already demonstrated how Amazon's facial analysis software, for example, distinguishes gender among certain ethnicities less accurately than other services, while Democratic presidential hopeful Senator Elizabeth Warren has called on federal agencies to address questions around algorithmic bias, such as how the Federal Reserve deals with money lending discrimination. Against this backdrop, "in-the-wild" software-testing company Applause is looking to "reinvent" AI testing with a new service that better detects AI bias by crowdsourcing larger training data sets. By way of a brief recap, Massachusetts-based Applause, formerly known as uTest, offers companies like Google and Uber a different kind of app-testing platform, one that taps hundreds of thousands of "vetted" real-world users around the world to squish bugs and iron out usability issues -- it's all about harnessing the power of the crowd rather than running tests entirely in contrived laboratory settings. The company had raised north of $115 million before it was acquired by investment firm Vista Equity Partners in 2017. A key facet of the Applause platform is not only the sheer number of crowd testers in its community, but the demographic diversity -- spanning language, race, gender, location, culture, hobbies, and more.

Testing specialists Applause have debuted an AI solution promising to help tackle algorithmic bias while providing the scale of data needed for robust training. Applause has built a vast global community of testers for its app testing solution which is trusted by brands including Google, Uber, PayPal, and more. The company is leveraging this relatively unique asset to help overcome some of the biggest hurdles facing AI development. AI News spoke with Kristin Simonini, VP of Product at Applause, about the company's new solution and what it means for the industry ahead of her keynote at AI Expo North America later this month. "Our customers have been needing additional support from us in the area of data collection to support their AI developments, train their system, and then test the functionality," explains Simonini.