With the advancement of technology, things are supposed to be getting easier. And while for some they are becoming swift, not all technology can be relied upon. Among the many technological developments is the facial recognition which is becoming of more common usage. This technology is also used by police departments and intelligence agencies to track criminals. But the latest report of Amazon's facial recognition tool Rekognition, has given shocking and erroneous results. It matched 28 Congress politicians faces with mugshots of criminals.

The American Civil Liberties Union (ACLU) pointed at the major weakness in this face surveillance technology. The technology which is considered to be reliable has been used in several businesses- home security company and also the Washington County Sheriff Office. With the wrong matches, it raises concern that it could even lead to false matches and the innocents would be punished. Now there is a growing opposition to Amazon's Rekognition and now there is even a petition to 'Tell Amazon to get out of the Surveillance Business.'

It was tested against Members of Congress in America and it matched 28 of them wrongfully with convicts. It matched the members against a database of criminal mugshots and 28 of them were incorrectly matched. While testing it, they also found racial bias in it. People of a darker complexion were matched incorrectly while the white faces were more accurate. A technology like this is used on a global level, so such discrepancies also need to be taken care of, rather eliminated.

The ACLU noted the concern with the system. It wrote, "An identification — whether accurate or not — could cost people their freedom or even their lives. People of color are already disproportionately harmed by police practices, and it's easy to see how Rekognition could exacerbate that."

(The above story first appeared on LatestLY on Jul 27, 2018 05:44 PM IST. For more news and updates on politics, world, sports, entertainment and lifestyle, log on to our website latestly.com).