As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people's moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person's assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people's unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people's fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.

One of this week's milestones in the history of technology sets the tone to two centuries of debating the impact of machines and artificial intelligence on human work and welfare. On February 27, 1812, Lord Byron gave his first address as a member of the House of Lords in a parliamentary debate on the Frame Breaking Act which made destroying or damaging lace-machines (stocking frames) a crime punishable by death. The rejected workmen, in the blindness of their ignorance, instead of rejoicing at these improvements in arts so beneficial to mankind, conceived themselves to be sacrificed to improvements in mechanism. In the foolishness of their hearts, they imagined that the maintenance and well doing of the industrious poor, were objects of greater consequence than the enrichment of a few individuals by any improvement in the implements of trade which threw the workmen out of employment, and rendered the labourer unworthy of his hire. And, it must be confessed, that although the adoption of the enlarged machinery, in that state of our commerce which the country once boasted, might have been beneficial to the master without being detrimental to the servant; yet, in the present situation of our manufactures, rotting in warehouses without a prospect of exportation, with the demand for work and workmen equally diminished, frames of this construction tend materially to aggravate the distresses and discontents of the disappointed sufferers.

We can think of privacy policies as fortresses made out of thick bricks of gobbledygook: impenetrable, sprawling documents that do little beyond legally protect companies. Or, to be more precise, 98% of people don't read them, according to one study, which led to 98% of volunteers signing away their firstborns and agreeing to have all their personal data handed over to the National Security Agency (NSA), in exchange for signing up to a fictional new social networking site. And here's the thing: if you're one of the everybody who doesn't read privacy policies, don't feel bad: it's not your fault. Online privacy policies are so cumbersome that it would take the average person about 250 working hours – about 30 full working days – to actually read all the privacy policies of the websites they visit in a year, according to one analysis. So how do we keep from signing away our unsuspecting tots?

A screencap posted on Chinese social network Weibo showed Microsoft-developed XiaoBing declaring that its "China dream is to go to America." The "girlfriend app" also brilliantly dodged a patriotic question by responding with: "I'm having my period, wanna take a rest." While these responses may seem like they can't hold a candle to Tay's racist and sexist tweets, they're the worst responses a chatbot could serve up in China. Especially now that authorities are tightening internet access even further and ramping up censorship leading to the Communist Party's leadership reshuffle this fall. As Financial Times points out, this is the latest instance that brings the flaws of deep learning techniques to the surface.

The researchers trained Polisis using data sets from 115 privacy policies, analyzed and annotated by Fordham Law students, and 130,000 others taken from Google Play Store apps. What makes this AI appealing is how it's able to transform a boring, wordy privacy policy into something that's digestible. And if users are still having trouble with aspects of the agreement, the researchers have another service on their website -- an AI-powered chatbot called PriBot that users can discuss privacy policies with.

Sometimes, the tide of technological innovation seems unstoppable. But tech companies still have to abide by laws, rules and regulations, like the rest of us. Moreover, governments and other regulatory bodies are increasingly concerned with ensuring that basic rights and liberties don't get washed away as the digital future rushes in. The General Data Protection Regulation (GDPR) provides a great example of how regulations will shape the development of powerful new technologies like big data analytics, machine learning and artificial intelligence. GDPR is a new European Union regulation that will come into force in late May 2018.

In the coming months, NYC Mayor Bill de Blasio will announce a new task force on "Automated Decision Systems" -- the first of its kind in the United States. The task force will recommend how each city agency should be accountable for using algorithms and other advanced computing techniques to make important decisions. As a first step toward this goal, we urge the task force to consider a framework structured around Algorithmic Impact Assessments (AIAs). Automated decision systems are here, and are already being integrated across many core social institutions, reshaping how our criminal justice system works via risk assessment algorithms and predictive policing systems, optimizing energy use in critical infrastructure through AI-driven resource allocation, and changing our educational system through new teacher evaluation tools and student-school matching algorithms. And these are merely what journalists, researchers, and the public record expose -- to date, no city in the US has explicitly mandated that its agencies disclose anything about the automated decision systems they have in place or are planning to use.

Despite efforts to tackle corruption around the world, progress is still frustratingly slow, according to the latest report from Transparency International. Its annual Corruption Perception index reveals some alarming trends. It shows public service corruption is still a huge problem for two-thirds of the world's economies. The report uses a scale of zero to 100 to rank countries: zero is highly corrupt and 100 is very clean. New Zealand comes out on top but with a score of 89.

The nature of work has historically been dependent on the roles we undertake, and the meanings of such roles for us as individuals fitting into a larger society. To many people, work is the means to a materialistic end, such as paying the bills. To others, it is a way of realising life's desires. In any case, technology is reshaping definitions of work. Particularly, artificial intelligence (AI) is automating many existing jobs, hence the human workforce is being redirected and retrained for new roles at the same time it seems at increasing risk of robotic replacement.

JD Schramm is the MBA Class of 1978 Lecturer in Organizational Behavior at Stanford's Graduate School of Business. Computers are more accurate than humans at visually detecting sexual orientation, according to an article in this month's Journal of Personality and Social Psychology. That's not really news to me -- I've always been bad with "gaydar." I was once on a third date with a guy before I knew he was gay. Okay, I did not realize our first two coffees were actually dates in his mind, and the friends who introduced us failed to tell me it was a romantic setup.