Google Street View image of a house predicts car accident risk of its resident and raises privacy concerns that legislation has yet to address via @benedictevans

Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for appropriate allocation of resources in healthcare, insurance, civil engineering, and other industries. We show how images of houses are predictive of car accidents. We analyze 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View, and annotate house features such as age, type, and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.

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WHY IT MATTERS: more proof that digital transformation will impact our lives in potentially dramatic ways. Here the use of public street view images of houses help predict the risk of car accident by their owner should not come as a surprise as the digitization of physical world assets provides troves of previously unavailable data for companies to mine and exploit. This quote rom the paper highlights the privacy concerns we should all have from this and other digital analysis: 

In particular, features of the house may be a proxy of ethnicity, religion or other characteristics associated with a social status of a person which are forbidden by the law to be used for any discrimination, e.g. price discrimination in certain jurisdictions. Fast development of modern data collection and computational techniques allows for unprecedented exploitation of various data of clients being not even aware of it and development of corresponding legislation in this matter seems to be outpaced.

Farid Mheir