Some parts of machine learning are incredibly esoteric and hard to grasp, surprising even seasoned computer science pros; other parts of it are just the same problems that programmers have contended with since the earliest days of computation. The problem Amazon had with its machine-learning-based system for screening job applicants was the latter. Read the rest
Research into the shittiness of voice assistants zeroed in on a problem that many people were all-too-aware of: the inability of these devices to recognize "accented" speech ("accented" in quotes because there is no one formally correct English, and the most widely spoken English variants, such as Indian English, fall into this "accented" category). Read the rest
Pete Warden writes convincingly about computer scientists' focus on improving machine learning algorithms, to the exclusion of improving the training data that the algorithms interpret, and how that focus has slowed the progress of machine learning. Read the rest
Social scientist Kate Crawford (previously) and legal scholar Ryan Calo (previously) helped organize the interdisciplinary White House AI Now summits on how AI could increase inequality, erode accountability, and lead us into temptation and what to do about it. Read the rest
Tim O'Reilly writes about the reality that more and more of our lives -- including whether you end up seeing this very sentence! -- is in the hands of "black boxes": algorithmic decision-makers whose inner workings are a secret from the people they affect. Read the rest
If you've read Cathy O'Neil's Weapons of Math Destruction (you should, right NOW), then you know that machine learning can be a way to apply a deadly, nearly irrefutable veneer of objectivity to our worst, most biased practices. Read the rest
Writing in Slate, Cathy "Weapons of Math Destruction" O'Neill, a skeptical data-scientist, describes the ways that Big Data intersects with ethical considerations. Read the rest
