Stanford scientists have used an artificial intelligence system and publicly available data from Google Street View to predict income levels and voting patterns of neighbourhoods in the US. If the number of sedans in a city is higher than the number of pickup trucks, that city is likely to vote for a Democrat in the next presidential election (88 per cent chance), showed the study published in the journal Proceedings of the National Academy of Sciences. “This kind of social analysis using image data is a new tool to draw insights,” Timnit Gebru, who led the research, was quoted as saying by ‘Tech Crunch’.
Helped by recent advances in artificial intelligence, researchers from Stanford University in the US collected details about cars in the millions of images, including makes and models. By linking the information with other data sources, the project was able to predict factors like pollution and voting patterns at the neighbourhood level. In the study led by Timnit Gebru of Stanford University, the researchers presented a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars.