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.
I was hugely impressed with Cathy "Mathbabe" O'Neil's talk at Personal Democracy Forum 2015, "Weapons of Math Destruction," in which she laid out the way that the "opaque, unregulated, and uncontestable" conclusions of Big Data threaten fairness and democracy. — Read the rest
Johann Carl Friedrich Gauss (1777-1855) is considered one of the greatest mathematicians in history. Born in Brunswick, Germany, he developed the method of least squares, proved the fundamental theorem of algebra, made significant contributions to electromagnetism, and invented the heliotrope (an optical communication device). — Read the rest
Cathy "Weapons of Math Destruction" O'Neil wants us to have empathy for Big Tech CEOs like Mark Zuckerberg and Jack Dorsey, who are "monumentally screwed, because they have no idea how to tame the monsters they have created."
Hirevue is an "AI" company that companies contract with to screen job applicants: it conducts an hour-long videoconference session with applicants, analyzing their facial expressions, word-choices and other factors (the company does not actually explain what these are, nor have they ever subjected their system to independent scrutiny) and makes recommendations about who should get the job.
The ever-useful Gartner Hype Cycle identified an inflection point in the life of any new technology: the "Peak of Inflated Expectations," attained just before the sharp dropoff into the "Trough of Disillusionment"; I've lived through the hype-cycles of several kinds of technology and one iron-clad correlate of the "Peak of Inflated Expectations" is the "Peak of Huckster Snakeoil Salesmen": the moment at which con-artists just add a tech buzzword to some crooked scam and head out into the market to net a fortune before everyone gets wise to the idea that the shiny new hypefodder isn't a magic bullet.
Kudos to the Gates Foundation, seriously: after spending $775m on the Intensive Partnerships for Effective Teaching, a Big Data initiative to improve education for poor and disadvantaged students, they hired outside auditors to evaluate the program's effectiveness, and published that report, even though it shows that the approach did no good on balance and arguably caused real harms to teachers and students.
The Electronic Frontier Foundation's Jamie Williams and Lena Gunn have drawn up an annotated five-point list of questions to ask yourself before using a machine-learning algorithm to make predictions and guide outcomes.
There are 50 hospitals on 5 continents that use Watson for Oncology, an IBM product that charges doctors to ingest their cancer patients' records and then make treatment recommendations and suggest journal articles for further reading.
Once big data systems agglomerate enough data about you to predict whether you are likely to get sick or badly injured, insurers will be able to deny coverage (or charge so much for it that it amounts to the same thing) to anyone who is likely to get sick, forcing everyone who might ever need insurance into medical bankruptcy, and turning Medicaid into a giant "high-risk pool" that taxpayers foot the bill for.
Nesta's Juan Mateos-Garcia proposes that "entropic forces" make algorithmic decision-making tools worse over time, requiring that they be continuously maintained and improved (this is also a key idea from Cathy O'Neil's Weapons of Math Destruction: a machine-learning system is only honest if someone is continuously matching its predictions to reality and refining its model based on the mistakes it makes).
An article that went viral last week attributed Trump's Electoral College victory to the dark big data sorcery of Cambridge Analytica, a dirty, dementor-focused big data company that specializes in political campaigns.
Social scientist/cybersecurity expert Susan Landau (previously) and Cathy "Weapons of Math Destruction" O'Neil take to Lawfare to explain why it would be a dangerous mistake for the FBI to use machine learning-based chatbots to flush out potential terrorists online.
The Data & Society institute (dedicated to critical, interdisciplinary perspectives on big data) held an online seminar devoted to Cathy O'Neil's groundbreaking book Weapons of Math Destruction, which showed how badly designed algorithmic decision-making systems can create, magnify and entrench the social problems they're supposed to solve, perpetuating inequality, destabilizing the economy, and making a small number of people very, very rich.
Here's this year's complete Boing Boing Gift Guide: more than a hundred great ideas for prezzies: technology, toys, books and more. Scroll down and buy things, mutants! Many of the items use Amazon Affiliate links that help us make ends meet at Boing Boing, the world's greatest neurozine. — Read the rest
When we got to rounding up our favorite books for our annual Gift Guide, we found that there were simply too many this time to throw in the Christmas/Kwanzaa/Hanukah/Yule/Solstice/Nonspecific Winter Celebration/New Year/Chalica hopper along with the tech and toys.
It's almost as if 2016 made the traditional way of learning more about our world — and of sharing dreams of other worlds — somehow more enticing. — Read the rest
The Guardian's published a long excerpt from Cathy O'Neil's essential new book, Weapons of Math Destruction, in which O'Neil describes the way that shoddy machine-learning companies have come to dominate waged employment hiring, selling their dubious products to giant companies that use them to decide who can and can't work.
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. 
