Applause targets AI bias by sourcing training data at scale

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.