Calculating Empires: an huge online chart of tech history

Calculating Empires

Calculating Empires is a "a genealogy of technology and power since 1500" — a beautiful and interactive monochrome chart you can zoom in and out of to trace the connections between all such things in the modern age. I immediately crash zoomed in and found myself face-to-face with a Debord quote: "In societies where modern conditions of production prevail, all of life presents itself as an immense accumulation of spectacles. — Read the rest

The third annual AI Now report: 10 more ways to make AI safe for human flourishing

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.

What is bias in machine learning, when does it matter, and what can we do about it?

"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

Forget Skynet: AI is already making things terrible for people who aren't rich white dudes

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.

White House contends with AI's big social challenges, July 7/NYC

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."