Next month, I'm giving a keynote talk at The Future of the Future: The Ethics and Implications of AI, an event at UC Irvine that features Bruce Sterling, Rose Eveleth, David Kaye, and many others! Read the rest

While NIST doesn't speculate as to why, it did find that the performance of 189 facial recognition algorithms from 89 different vendors varied by "race, sex and age" -- that is, the systems performed significantly worse when asked to recognize people who weren't young, white and male. Read the rest

More bad news for Google's beleaguered spinoff Jigsaw, whose flagship project is "Perspective," a machine-learning system designed to catch and interdict harassment, hate-speech and other undesirable online speech. Read the rest

I used to be on the program committee for the O'Reilly Emerging Technology conferences; one year we decided to make the theme "magic" -- all the ways that new technologies were doing things that baffled us and blew us away. Read the rest

"Predictive policing" is the idea that you can feed crime stats to a machine-learning system and it will produce a model that can predict crime. It is garbage. Read the rest

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

I've been writing about the work of Cathy "Mathbabe" O'Neil for years: she's a radical data-scientist with a Harvard PhD in mathematics, who coined the term "Weapons of Math Destruction" to describe the ways that sloppy statistical modeling is punishing millions of people every day, and in more and more cases, destroying lives. Today, O'Neil brings her argument to print, with a fantastic, plainspoken, call to arms called (what else?) Weapons of Math Destruction.

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