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Nice discussion on privacy, "How much do you value your privacy? Does it bother you what social media companies, governments know about you - your money, your body?"

In August 2016, the Australian government released an "anonymised" data set comprising the medical billing records, including every prescription and surgery, of 2.9 million people. Names and other identifying features were removed from the records in an effort to protect individuals' privacy, but a research team from the University of Melbourne soon discovered that it was simple to re-identify people, and learn about their entire medical history without their consent, by comparing the dataset to other publicly available information, such as reports of celebrities having babies or athletes having surgeries.

Most people pass through some type of public space in their daily routine - sidewalks, roads, train stations. Thousands walk through Bryant Park every day. But we generally think that a detailed log of our location, and a list of the people we're with, is private. Facial recognition, applied to the web of cameras that already exists in most cities, is a threat to that privacy. To demonstrate how easy it is to track people without their knowledge, we collected public images of people who worked near Bryant Park (available on their employers' websites, for the most part) and ran one day of footage through Amazon's commercial facial recognition service.

Excellent NYT interactive - where do you draw the line on how your data is used by social media companies and smart devices?

Nearly every ad you see online is tailored just for you. These digital ads are powered by vast, hidden datasets that allow advertisers to make eerily accurate guesses about who you are, where you've been, how you feel and what you might do next. While targeted ads may be familiar by now, how they work - and the power they have - often seems invisible. We decided to lift the veil on this part of the internet economy, so we bought some ad space. We picked 16 categories (like registered Democrats or people trying to lose weight) and targeted ads at people in them. But instead of trying to sell cars or prescription drugs, we used the ads to reveal the invisible information itself.

One way of working out if the data you're gathering is particularly sensitive is to do a thought experiment: what would happen if this data got into the hands of a malicious actor? Who would be keen to get their hands on it? What are the worst things that they could do with this data? Sometimes, though, it can be hard to put yourself in the shoes of your enemies, or to envision potential future actions. As a result, practising data minimisation is a keystone of a rights-based, responsible data approach. And sadly, it's the opposite of the approach we're seeing governments around the world take.

Australian farms generate huge volumes of agricultural data. Examples include the types of crops being grown, crop yields, livestock numbers and locations, types of fertilisers and pesticides being used, soil types, rainfall and more. This data is typically collected through the use of digital farming machinery and buildings featuring robotics and digital technologies, artificial intelligence, and devices connected to the internet ("internet of things", or IoT). But a recent review from the Australian Bureau of Statistics and the Australian Bureau of Agricultural and Resource Economics highlights the patchy and fragmented nature of existing government and industry approaches to agricultural data. What that means is Australian farmers are currently not adequately protected from their farm data being collected and used without their knowledge or consent.

Last month, CNBC reported on a page in Google's account settings titled "Purchases" - a month-by-month list of items you've bought across online services like Amazon and other apps that are collected via Google services like Gmail. Purchases is a jarring example of how leaky our data really is and how large companies can aggregate that information unbeknown to the consumer. I, for one, was unaware that almost every concert ticket, Domino's pizza and Amazon purchase (including a 2014 accidental purchase of the film "Tango & Cash") was being logged by Google. Equally troubling: The purchases can't easily be deleted from the page without also deleting the receipt emails from your Gmail account.

"Anonymised" data lies at the core of everything from modern medical research to personalised recommendations and modern AI techniques. Unfortunately, according to a paper, successfully anonymising data is practically impossible for any complex dataset.

Interesting article about how different kinds of data (like your social media data) can give insights into health, but don't have the same protections as health data

A series of short articles, with videos and browser addons "investigating algorithmic injustice and the formulas that influence our lives."

When creating a machine-learning algorithm, designers have to make many choices: what data to train it on, what specific questions to ask, how to use predictions that the algorithm produces. These choices leave room for discrimination, particularly against people who have been discriminated against in the past. For example, training an algorithm to select potential medical students on a data set that reflects longtime biases against women and people of color may make these groups less likely to be admitted. In computing, the phrase "garbage in, garbage out" describes how poor-quality input leads to poor-quality output. In this case we might say, "White male doctors in, white male doctors out."

Blame the algorithm. That's become the go-to refrain for why your Instagram feed keeps surfacing the same five people or why YouTube is feeding you questionable "up next" video recommendations. But you should blame the algorithm - those ubiquitous instructions that tell computer programs what to do - for more than messing with your social media feed. Algorithms are behind many mundane, but still consequential, decisions in your life. The code often replaces humans, but that doesn't mean the results are foolproof. An algorithm can be just as flawed as their human creators. These are just some of the ways hidden calculations determine what you do and experience.