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Applause targets AI bias by sourcing training data at scale

[2019.11.07, Thu 15:05] 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. This will likely be among the main selling points as Applause looks to reappropriate its technology to offer companies access to diverse AI training data. "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," said Applause product VP Kristin Simonini. Applause's AI training and testing service is offered across five core AI types covering voice, optical character recognition, image recognition, biometrics, and chatbots. If, for example, a company needs to quickly source varied training data for a virtual voice assistant, Applause users in various locales could be called upon to record and submit specific utterances. Applause promises speed and scale for both gathering training data and testing the outputs, allowing companies to garner rapid and iterative feedback from end users in real time. Similar initiatives out there at the moment include Amazon's Mechanical Turk, which can be used to crowdsource data for machine learning experiments; DefinedCrowd, which helps create bespoke data sets for AI model training; and Germany's Clickworker, which specifically focuses on machine vision and conversational AI. Thanks to more than a decade of software testing with some of the biggest tech companies in the world Applause is well-positioned to harness its existing presence in the developer community and offer vetted crowd testers to improve AI applications by reducing bias.
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