Radha Basu has long used technology to push socio-economic boundaries. After a long stint at HP, she led SupportSoft to IPO as CEO. Radha then launched Anudip Foundation to create digital livelihoods in India. In 2012, Radha founded iMerit, which enriches data for machine learning, computer vision and e-commerce. 80% of iMeriters come from poor backgrounds. 50% are women. Radha has won awards like UN Women-ITU Award and Top 25 Women of the Web. She created the Frugal Innovation Lab at Santa Clara University. Radha speaks extensively on the Future of AI Livelihoods and Digital Inclusion.
Algorithm-focused technologies disproportionately make life easier for wealthy people. The people who build machine learning algorithms are typically at companies where they get to take economic advantage of deployed machine learning models for years. Training data-focused technologies, by contrast, disproportionately squeeze value out of less wealthy people, who are paid only once and don't get to take advantage of the success of models that used their training data.
This talk will focus on methods that ensure the fair creation of training data for machine learning, whether the annotators are in-house, contracted, or work as crowdsourced workers online. It will show that contrary to the widely-held belief that training data creation is a race to the bottom in pricing, it is possible to maximize quality and fairness at the same time for almost any machine learning task.