Another woman discovered that the search "unprofessional hairstyles for work" yielded images of black women while "professional hairstyles for work" brought up images of white women. In 2015, users discovered that searching for "n*gga house" in Google Maps directed users to the White House. That same year, a tool that automatically categorizes images in the Google Photos app tagged a black user and his friend as gorillas, a particularly egregious error considering that comparison is often used by white supremacists as a deliberately racist insult. Camera companies like Kodak sold film that photographed white skin better than black skin, and companies like Nikon have also shown racial bias toward Caucasian features in their facial-recognition technology.
In the new study, computer scientists replicated many of those biases while training an off-the-shelf machine learning AI on a "Common Crawl" body of text--2.2 million different words--collected from the Internet. To reveal the biases that can arise in natural language learning, Narayanan and his colleagues created new statistical tests based on the Implicit Association Test (IAT) used by psychologists to reveal human biases. Their work detailed in the 14 April 2017 issue of the journal Science is the first to show such human biases in "word embedding"--a statistical modeling technique commonly used in machine learning and natural language processing. Narayanan and his colleagues at Princeton University and University of Bath in the U.K. first developed a Word-Embedding Association Test (WEAT) to replicate the earlier examples of race and gender bias found in past psychology studies.
Mind-reading algorithms that use machine learning to reconstruct brain activity could reveal our innermost thoughts and could turns our society into a'Big Brother' world Experts from the University of Cambridge explore the uses of mind-reading algorithms and find the technology will be successful as a lie detector - which is already being tested. The typical accuracy reported in the literature is around 90%, meaning that nine out of ten times, the computer correctly classified answers as lies or truths. Then machine learning algorithms were used to predict face components from fMRI activity patterns and reconstruct images of individual faces in digital portraits. There are problems with this technology because fMRI involves lying still in a big noise tube (pictured) for long periods of time.
Concerns have been growing about AI's so-called "white guy problem" and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making. Mortiz Hardt, a senior research scientist at Google and a co-author of the paper, said: "Decisions based on machine learning can be both incredibly useful and have a profound impact on our lives ... The test is aimed at machine learning programs, which learn to make predictions about the future by crunching through vast quantities of existing data. 'I think my blackness is interfering': does facial recognition show racial bias?
Concerns have been growing about AI's so-called "white guy problem" and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making. Mortiz Hardt, a senior research scientist at Google who led the work, said: "Decisions based on machine learning can be both incredibly useful and have a profound impact on our lives ... The test is aimed at machine learning programs, which learn to make predictions about the future by crunching through vast quantities of existing data. 'I think my blackness is interfering': does facial recognition show racial bias?
Courts even deploy computerized algorithms to predict "risk of recidivism", the probability that an individual relapses into criminal behavior. Given years of credit history and other side information, a machine learning algorithm might then output a probability that the applicant will default. Machine learning refers to powerful set of techniques for building algorithms that improve as a function of experience. When Facebook recognizes your face in a photograph, when your mailbox filters spam, and when your bank predicts default risk – these are all examples of supervised machine learning in action.
It's no secret that there is a wide gender gap in the tech industry. According to the Center for the Study of the Workplace, women represent around 20 percent of engineering graduates, but just 11 percent of practicing software engineers. Unconscious bias is one of the primary drivers of this disparity, which has led many of Silicon Valley's leading tech companies to introduce unconscious bias training to their employees. However, it's fair to say that its machine learning algorithms need it more.