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An Israeli startup founded in 2014, Zebra Medical Vision, develops algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making diagnoses. By July 2018, Zebra had developed seven algorithms to analyze scans for emphysema, liver density, compression fractures, bone density, brain bleeds, breast cancer, and a calcium score-used to detect calcified plaque in coronary arteries. For each scan analyzed, Zebra charged hospitals $1. By 2018 Zebra found itself in a race with its competitors to perfect these algorithms, create software tools, distribute the tools to physician partners, and create a market. Zebra already had several partners in the U.S. and Europe who gave feedback on its development. Management had to decide: What should they do next at Zebra? Should they work on the accuracy of the already-developed algorithms, or continue to develop many new tools? If they chose to develop new products, which applications should they address? And how should they go to market?

Joe Morrisroe, executive director for NYC311, had some gut instincts but no definitive answer to the question he was just asked by one of the Mayor's deputies: "Are some communities being underserved by 311? How do we know we are hearing from the right people?" Founded in 2003 as a phone number for residents to dial (311) from a landline for information on city services and to log complaints, the city launched a 311 website and mobile app in 2009, and social media support in 2011. In 2016, NYC311 received over 35 million requests for services and information. Technological progress had made it considerably easier to hear from NYC residents. Were those gains from innovation being shared equally? More recently, the city began using the data to create predictive models that might help direct inspectors and other workers. Morrisroe and his team had considered the potential downsides of agencies relying too heavily on NYC311 data or on its predictive power. In the sheer volume of the data and its potential to enable a new approach to city services, were biases around income, education, race, gender, neighborhood, home ownership, and other factors, hiding too? Morrisroe considered the question posed to him and its implications. He asked for the data and a team to assess it: Are we hearing from everyone?