One of the biggest civil liberties issues raised by technology today is whether, when, and how we allow computer algorithms to make decisions that affect people’s lives. We’re starting to see this in

Share Lab uses flow charts and data analysis to map one of the greatest forces shaping our world - Facebook.

And now critics worry about what comes next.

The ever-expanding operations of Uber are defined by two interlocking and zealously guarded sets of information: the things the world-dominating ride-hailing company knows about you, and the things it doesn’t want you to know about it. Both kinds of secrets have been in play in the Superior Court of California in San…

This Article examines the matrix of vulnerabilities that low-income people face as a result of the collection and aggregation of big data and the application of

Here’s one way to confuse it.

"We TOLD you it was hard." "Yeah, but now that I'VE tried, we KNOW it's hard."

Deep learning algorithms are often trained on data from the web, and their biases are getting hard to ignore

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.

This paper studies emerging technologies for tracking reading behaviors (“reading analytics”) and their implications for reader privacy, attempting to place them in a historical context. It discusses what data is being collected, to whom it is available, and how it might be used by various interested parties (including authors). I explore means of tracking what’s being read, who is doing the reading, and how readers discover what they read. The paper includes two case studies: mass-market e-books (both directly acquired by readers and mediated by libraries) and scholarly journals (usually mediated by academic libraries); in the latter case I also provide examples of the implications of various authentication, authorization and access management practices on reader privacy. While legal issues are touched upon, the focus is generally pragmatic, emphasizing technology and marketplace practices. The article illustrates the way reader privacy concerns are shifting from government to commercial surveillance, and the interactions between government and the private sector in this area. The paper emphasizes U.S.-based developments.

Sunlight is said to be the best of disinfectants…

The Long Read: The ability of statistics to accurately represent the world is declining. In its wake, a new age of big data controlled by private companies is taking over – and putting democracy in peril

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,

Algorithmic systems have a way of making mistakes or leading to undesired consequences. Here are five principles to help technologists deal with that.

Why were no librarians involved in New York's sidewalk Internet-access kiosks?

Update– Please see follow-on posts addressing changes since this post: Nov 13, 2016: “Piazza Response To Blog Post On Student Privacy” Nov 21, 2016: “University Responses to Piazza: Some good, some bad, some web site changes” Dec 21, 2016: “Piazza Makes Three Significant Changes To Deal With Privacy Issues” Piazza is a collaborative question-and-answer platform […]

Insurer withdraws initiative with hours to go as privacy campaigners criticise ‘intrusive’ attempt to analyse users’ data