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AI generated images are biased, showing the world through stereotypes

Fighting algorithmic bias in artificial intelligence – Physics World

Why Silicon Valley's most astute critics are all women | Technology

Tailors and dressmakers long ago worked out that men and women are different shapes and sizes. The news has yet to reach Palo Alto Critical voice: Professor Carissa Véliz, author of Privacy Is Power, a study of the personal data we have surrendered to the tech giants. Photograph: Fran Monks Critical voice: Professor Carissa Véliz, author of Privacy Is Power, a study of the personal data we have surrendered to the tech giants. Photograph: Fran Monks Sat 3 Apr 2021 11.00 EDT In November 2019, which now seems like an aeon ago, I wrote about an interesting correlation I had stumbled across. It was that the authors of the most insightful critiques of digital technology as deployed by the tech companies were women. I listed 20 of them and added that I made no claims for the statistical representativeness of my sample. It might simply have been the result of confirmation bias – I read more tech commentary than is good for anyone and it could be that the stuff that sticks in my mem

Developing Algorithms That Might One Day Be Used Against You

Developing Algorithms That Might One Day Be Used Against You Photo: Mark Lopez/Argonne National Laboratory Machine learning algorithms serve us the news we read, the ads we see, and in some cases even drive our cars. But there’s an insidious layer to these algorithms: They rely on data collected by and about humans, and they spit our worst biases right back out at us. For example, job candidate screening algorithms may automatically reject names that sound like they belong to nonwhite people, while facial recognition software is often much worse at recognizing women or nonwhite faces than it is at recognizing white male faces. An increasing number of scientists and institutions are waking up to these issues, and speaking out about the potential for AI to cause harm.

Medicine's Machine Learning Problem

As Big Data tools reshape health care, biased datasets and unaccountable algorithms threaten to further disempower patients. Data science is remaking countless aspects of society, and medicine is no exception. The range of potential applications is already large and only growing by the day. Machine learning is now being used to determine which patients are at high risk of disease and need greater support (sometimes with racial bias), to discover which molecules may lead to promising new drugs, to search for cancer in X-rays (sometimes with gender bias), and to classify tissue on pathology slides. Last year MIT researchers trained an algorithm that was more accurate at predicting the presence of cancer within five years of a mammogram than techniques typically used in clinics, and a 2018 survey found that 84 percent of radiology clinics in the United States are using or plan to use machine learning software. The sense of excitement has been captured in popular books such as Eric Topo