Online argumentation, particularly on popular public discussion boards and social media, is rich with fallacy-and bias-prone arguments. An artificially intelligent tool capable of identifying potential biases in online argumentation might be able to address this growing problem, but what would it take to develop such a tool? In this paper, we attempt to answer this question by carefully defining both argumentative biases and fallacies, and laying out some guidelines for automated bias detection. After laying out a roadmap and identifying current bottlenecks, we take some initial steps towards relieving these limitations through the creation of a dataset of personal and ad hominem attacks in comments. Our progress in this direction is summarized.

When a semi-autonomous car crashes and harms someone, how are blame and causal responsibility distributed across the human and machine drivers? In this article, we consider cases in which a pedestrian was hit and killed by a car being operated under shared control of a primary and a secondary driver. We find that when only one driver makes an error, that driver receives the blame and is considered causally responsible for the harm, regardless of whether that driver is a machine or a human. However, when both drivers make errors in cases of shared control between a human and a machine, the blame and responsibility attributed to the machine is reduced. This finding portends a public under-reaction to the malfunctioning AI components of semi-autonomous cars and therefore has a direct policy implication: a bottom-up regulatory scheme (which operates through tort law that is adjudicated through the jury system) could fail to properly regulate the safety of shared-control vehicles; instead, a top-down scheme (enacted through federal laws) may be called for.

There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the black box of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR. We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system.

All eyes are on Facebook as more and more information rolls out regarding Cambridge Analytica, its involvement in recent elections and forums and how it came to obtain 50 million Facebook users' profile information. Now, New York Attorney General Eric Schneiderman is joining those demanding more information from the social network giant. "Consumers have a right to know how their information is used -- and companies like Facebook have a fundamental responsibility to protect their users' personal information," Schneiderman said in a statement. "Today, along with Massachusetts Attorney General Healey, we sent a demand letter to Facebook -- the first step in our joint investigation to get to the bottom of what happened."

The media is replete with articles about how artificial intelligence (AI) is going to change the medical world, in cancer detection and other diagnostic and treatment disciplines. The articles describe how AI, primarily deep learning (DL) applications are as accurate or better than medical experts. That means they'll be used quickly adopted, right? Not really, there's a regulatory picture many ignore. One of the first expert systems, a subset of AI, was MYCIN, initially developed as a doctoral dissertation by Edward Shortliffe, at Stanford University.

A disgruntled Minecraft gamer is believed to be behind a bomb hoax email sent to more than 400 schools and colleges. Some students were evacuated from school and college buildings across the country on Monday after an email threatening to detonate a bomb if they refused to hand over cash was sent out. The email appeared to come from gaming network VeltPvP – a server which allows users to compete in the game Minecraft – but the US company said that the account had been "spoofed". Carson Kallen, the US firm's 17-year-old CEO, told the BBC he suspected the hoax emails had been sent by a disgruntled Minecraft player in a bid to damage VeltPvP's reputation. He said: "Everyone who plays it is between the ages of eight and 18 years old - it's all kids playing. "Every now and then we have a little rebel who will try to do something bad like this.

Innovation is good; financial innovation is bad. It gave us the global financial crisis, after all, along with multibillion-dollar bailouts for entities such as AIG Financial Products, which almost nobody had heard of before they suddenly turned out to pose a mortal threat to the entire economy. That said, there are two financial innovations that are generally considered to have been clearly positive for society. One is the ATM, for reasons which should be self-explanatory. The other is passive investing.

StackOverflow recently asked 100,000 developers to participate in a 30-minute, wide-ranging survey that hits on just this. It's worth noting that the developers themselves were every bit as homogenous as you might expect–93% male, 74% white, 93% heterosexual, about half between the ages of 24 and 35. But their answers are a revealing look into how people writing code think about AI–namely, that they're concerned about its impact on society. This year we saw many designers offering a mea culpa, admitting that they were responsible for dark patterns and other manipulative bits of UI that shape human behavior in bad ways. So what do the surveyed developers think about their own role in creating tech, including AI?

This is a bitter-sweet technological advancement to say the least… With all the great things AI is said to bring, there are undoubtedly associated risks. As artificial intelligence and machine learning continues to gather momentum, so too does its ability to play a critical role in combatting cybercrime. There are forecasts that machine learning models will be able to predict and accurately identify attacks so swiftly, that cybercrime will effectively be stopped before it even begins. On the flip side, artificial intelligence also poses the risk of being exploited by cybercriminals, with experts worrying that it will be utilised as a dangerous tool in executing major, wide-spread attacks. We're hopeful that by the time this is feasible, extensive risk management will be in place to take care of this uncertainty.

NPR's Mary Louise Kelly speaks with Pennsylvania Attorney General Josh Shapiro about his office's intent to look into how the data of 50 million Facebook users got into the hands of the political data-mining firm, Cambridge Analytica.