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The conceptual, practical, and ethical issues surrounding "big data" and data in general begin at the very moment of data collection. Particularly when the data concern people, not enough attention is paid to the realities entangled within that significant moment and spreading out from it.1. Data sets are the results of their means of collection. It's easy to forget that the people collecting a data set, and how they choose to do it, directly determines the data set. An illustrative example can be found in the statistics for how many hate crimes were committed in the United States in 2012. According to the FBI Uniform Crime Reporting Program (UCR), the number was 5,796. However, the Department of Justice's Bureau of Statistics reported 293,800 hate crimes.

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.

Here's a discussion of how data science techniques that look at the kinds of things you watch, and try and make recommendations or customise based on that can work, and what might be problematic about that. "Some are noticing Netflix's tendency to entice black users with movie posters featuring black actors, no matter how minor their role in the film."

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.

Great podcast from our own 2ser & UTS Business School! "latest episode of #ThinkBusinessFutures @2ser, with Dr Geetanjali Saluja @UTSMarketing @UTS_Business, discussing her research into moral decision making, and Adam Ferrier, author of 'The Advertising Effect: How to Change Behaviour'" Discusses some of the framing, cognitive bias, and their impact on decision making that we talked about in class

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."

Algorithms decide which applications reach human managers' eyes. But they sort out people with unusual work histories or who lack college degrees

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."