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The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to "debias" the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.

"When a rogue researcher last week released 70,000 OkCupid profiles, complete with usernames and sexual preferences, people were pissed. When Facebook researchers manipulated stories appearing in Newsfeeds for a mood contagion study in 2014, people were really pissed. OkCupid filed a copyright claim to take down the dataset; the journal that published Facebook's study issued an "expression of concern." Outrage has a way of shaping ethical boundaries. We learn from mistakes."

"At the end of last year, between 21st November and 19th December, Transport for London carried out an intriguing trial: It was going to track your phone on the London Underground. Today, thanks to the Freedom of Information Act, Gizmodo UK can exclusively reveal some of the utterly fascinating findings that the agency has been able to make from all of our data "

Long Island resident said her web search history and 'trying to learn how to cook lentils' prompted a visit from authorities but police say search was prompted by tipoff

Twitter archives are a rich source of data for doing research into numerous things: Learning about social media and interaction networks, gaining insights into movement patterns based on geolocations and even doing sentiment analysis based on the tweets. And the best part of it: Unless you have a protected Twitter account this data is already public. So why not share it? The TwArχiv takes in your Twitter archive and generates interesting visualizations from your own tweets, including tweet volume over time and your interaction/movement patterns.

In support of our mission to benefit people and society, the Partnership on AI intends to conduct research, organize discussions, share insights, provide thought leadership, consult with relevant third parties, respond to questions from the public and media, and create educational material that advances the understanding of AI technologies including machine perception, learning, and automated reasoning.

Build your own smart speaker and learn AI literacy at the same time

London's Metropolitan Police have been urged to back down on plans to once again use facial recognition software at next weekend's Notting Hill Carnival. Privacy groups including Big Brother Watch, Liberty and Privacy International have written to police commissioner Cressida Dick (PDF) calling for a U-turn on the use of the tech. Automated facial recognition technology will snap the party-goers' faces, and run them against a database. The aim is to alert cops to people who are banned from the festival or are wanted by the police, presumably so they can take immediate action. The tech was first tested at the festival - where relations between police and revellers are often strained - last year, but it failed to identify anyone.