Computer algorithms now shape our world in profound and mostly invisible ways. They predict if we’ll be valuable customers and whether we’re likely to repay a loan. They filter what we see on social media, sort through resumes, and evaluate job performance. They inform prison sentences and monitor our health. Most of these algorithms have been created with good intentions. The goal is to replace subjective judgments with objective measurements. But it doesn’t always work out like that.
The thorny issue of tracking of location data without risking individual privacy is very neatly illustrated via a Freedom of Information (FOI) request asking London’s transport regulator to release the “anonymized” data-set it generated from a four week trial last year when it tracked metro users in the UK capital via wi-fi nodes and the MAC address of their smartphones as they traveled around its network.
In this article, we examine a range of longterm data collections, conducted by researchers in social science, in order to identify the characteristics of these programs that drive their unique sets of risks and benefits. We also examine the practices that have been established by social scientists to protect the privacy of data subjects in light of the challenges presented in long-term studies. We argue that many uses of big data, across academic, government, and industry settings, have characteristics similar to those of traditional long-term research studies. In this article, we discuss the lessons that can be learned from longstanding data management practices in research and potentially applied in the context of newly emerging data sources and uses.
Using code and the web, a data scientist follows two unnamed people and learns just how much our anonymous location data can say about who we are.
Artificial intelligence keeps getting creepier. In one controversial study, researchers at Stanford University have demonstrated that facial recognition technology can identify gay people with surprising precision, although many caveats apply. Imagine how that could be used in the many countries where homosexuality is a criminal offense.
Exposed! A Survey of Attacks on Private DataPrivacy-preserving statistical data analysis addresses the general question of protecting privacy when publicly releasing information about a sensitive dataset. A privacy attack takes seemingly innocuous released information and uses it to discern the private details of individuals, thus demonstrating that such information compromises privacy.
More social scientists are using AI intending to solve society’s ills, but they don’t have clear ethical guidelines to prevent them from accidentally harming people.
The widespread deployment of artificial intelligence and specifically machine learning algorithms causes concern for some fundamental values in society, such as employment, privacy, and discrimination.
The Information Commissioner’s Officer (ICO) ruled, on 3 July 2017, that the Royal Free NHS Foundation Trust (the Trust) had failed to comply with the Data Protection Act 1998 (DPA) when it provided 1.6 million patient details to Google DeepMind as part of a trial diagnosis and detection system for acute kidney injury, and required the Trust to sign an undertaking.