Public Service Internet

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.

"A trove of internal documents sheds light on the algorithms that Facebook's censors use to differentiate between hate speech and legitimate political expression."

"Nearly three years after the company was called out, it hasn't gone beyond a quick workaround"

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.

Open Humans is a platform that allows you to upload, connect, and privately store your personal data - such as genetic, activity, or social media data. Once you've added data, you can to donate it: you might choose to share some publicly , and you can join and contribute to diverse research projects. Thus, we turn the traditional research pipeline on its head: you are at the center and in control of when you share your data. We want to empower you to explore your data

Build your own web of things gateway

"In December, I converted my one-bedroom apartment in San Francisco into a "smart home." I connected as many of my appliances and belongings as I could to the internet: an Amazon Echo, my lights, my coffee maker, my baby monitor, my kid's toys, my vacuum, my TV, my toothbrush, a photo frame, a sex toy, and even my bed."

"Data Sense is a research experiment at Intel Labs. We wanted to see if it is possible to make data more accessible to those of us without stats degrees. To test out some ideas, we built this tool. "

"SoLiD is a proposed set of conventions for building decentralized social applications on the Linked Data stack. SoLiD is modular and extensible. It relies as much as possible on existing W3C standards. SoLiD applications are somewhat like multiuser applications where instances talk to each other through a shared filesystem, and the Web is that filesystem. "

"The BBC is doing cutting-edge research into Visual Perceptive Media, virtual reality and facial coding technologies. But do we want our shows to be tailored to our age, gender, and tastes? And what happens to all that data?"

Betty is a friendly English-like interface for your command line.

Open source, privacy aware Google Home & Amazon Alexa iot device and much more

Researchers say Google's ad-targeting system sometimes makes troubling decisions based on data about gender and other personal characteristics.

These principles were developed in conjunction with the 2017 Asilomar conference

"This is one of the minor dangers of owning an Amazon Echo. Most anyone can activate the device by just saying the wake word, including Cartman. And in the season opener of South Park, that's exactly what happened. If someone watched that episode of South Park in the same room as an Echo, their Amazon shopping list was filled with random, gross items. This example shows the potential danger of having a voice-activated shopping assistant. It's easy to imagine a potential rogue advertisement, online or elsewhere, that could, in theory, say the right words to order a particular product - like a South Park box set."

Kromtech Security Researchers have discovered another publically accessible Amazon S3 repository. This time it contained medical data in 316,363 PDF reports in the form of weekly blood test results. Many of these were multiple reports on individual patients. It appears that each patient had weekly test results totaling around 20 files each. That would still be an estimated 150,000+ people affected by the leak.

A couple of years ago, I was heavily involved in analysing and reporting on the massive VTech hack, the one where millions of records were exposed including kids' names, genders, ages, photos and the relationship to parents' records which included their home address. Part of this data was collected via an IoT device called the InnoTab which is a wifi connected tablet designed for young kids; think Fisher Price designing an iPad... then totally screwing up the security.

Sex toy company admits its vibrators secretly recorded intimate sessions

"Android phones gather your location data and send it to Google, even if you've turned off location services and don't have a SIM card"