"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.
Every year, NYU's nonprofit, critical activist group AI Now releases a report on the state of AI, with ten recommendations for making machine learning systems equitable, transparent and fail-safe (2016, 2017); this year's report just published, written by a fantastic panel, including Meredith Whittaker (previously — one of the leaders of the successful googler uprising over the company's contract to supply AI tools to the Pentagon's drone project); Kate Crawford (previously — one of the most incisive critics of AI); Jason Schultz (previously — a former EFF attorney now at NYU) and many others.
"The Trouble with Bias," Kate Crawford's (previously) keynote at the 2017 Neural Information Processing Systems is a brilliant tour through different ways of thinking about what bias is, and when we should worry about it, specifically in the context of machine learning systems and algorithmic decision making — the best part is at the end, where she describes what we should do about this stuff, and where to get started. — 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.
Writing on Medium, AI researcher Kate Crawford (previously) and Simply Secure (previously) co-founder Meredith Whittaker make the case for a new scholarly discipline that "measures and assesses the social and economic effects of current AI systems."
Kate Crawford (previously) takes to the New York Times's editorial page to ask why rich white guys act like the big risk of machine-learning systems is that they'll evolve into Skynet-like apex-predators that subjugate the human race, when there are already rampant problems with machine learning: algorithmic racist sentencing, algorithmic, racist and sexist discrimination, algorithmic harassment, algorithmic hiring bias, algorithmic terrorist watchlisting, algorithmic racist policing, and a host of other algorithmic cruelties and nonsense, each one imbued with unassailable objectivity thanks to its mathematical underpinnings.
Meredith from Simply Secure writes, "Artificial Intelligence is already with us, and the White House and New York University's Information Law Institute are hosting a major public symposium to face what the social and economic impacts might be. AI Now, happening July 7th in New York City, will address the real world impacts of AI systems in the next next 5-10 years."
Laura Poitras is the Macarthur-winning, Oscar-winning documentarian who made Citizenfour. Her life has been dogged by government surveillance and harassment, and she has had to become a paranoid OPSEC ninja just to survive.
Boing Boing is proud to publish two original documents disclosed by Edward Snowden, in connection with "Sherlock Holmes and the Adventure of the Extraordinary Rendition," a short story written for Laura Poitras's Astro Noise exhibition, which runs at NYC's Whitney Museum of American Art from Feb 5 to May 1, 2016.
The theory of Big Data is that the numbers have an objective property that makes their revealed truth especially valuable; but as Kate Crawford points out, Big Data has inherent, lurking bias, because the datasets are the creation of fallible, biased humans. — Read the rest
The Washington Post today published several big scoops related to the National Security Agency's surveillance programs. The paper's investigations were triggered by documents leaked to them "earlier this summer" by former NSA contractor Edward Snowden. He has sought political asylum from a number of nations, and is currently in Moscow. — Read the rest