This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility on her own opinion, and show that this general model is analogous to the Friedkin-Johnsen model, which assumes a constant susceptibility for each individual. We then consider two specific functions in which the individual's susceptibility depends on the \emph{polarity} of her opinion, and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. A court jury is used as a motivating example. Second, we consider stubborn neutrals, who have reduced susceptibility when their opinions are in the middle of the spectrum, and our motivating examples are social networks discussing established social norms or institutionalized behavior. For each specific susceptibility model, we establish the initial and graph topology conditions in which consensus is reached, and develop necessary and sufficient conditions on the initial conditions for the final consensus value to be at either extreme of the opinion interval. Simulations are provided to show the effects of the susceptibility function when compared to the DeGroot model.

Is it possible to predict the outcomes of legal cases – such as Supreme Court decisions – using Artificial Intelligence (AI)? I recently had the opportunity to consider this point at a talk that I gave entitled "Machine Learning Within Law" at Stanford. The general idea behind such approaches is to use computer-based analysis of existing data (e.g. The approach to using data to inform legal predictions (as opposed to pure lawyerly analysis) has been largely championed by Prof. Katz – something that he has dubbed "Quantitative Legal Prediction" in recent work.

Legal artificial intelligence (AI) was a major theme at this year's International Legal Technology Association Conference (ILTACon 2017). At ILTACon, law firm IT and a small group of in-house lawyers, network, learn from each other, and attend educational sessions. It is an amazing opportunity to catch up on legal technology. This year my radar was searching for an update on what is really happening with legal AI in law firms. Are law firms getting beyond the hype and using AI?

WIRE)--The Industrial Internet Consortium (IIC), the world's leading organization transforming business and society by accelerating the adoption of the Industrial Internet of Things (IIoT), today announced the Smart Factory Machine Learning for Predictive Maintenance Testbed. The testbed is led by two companies, Plethora IIoT, a company, designing and developing cutting-edge answers for Industry 4.0, and Xilinx, the leading provider of All Programmable technology. This innovative testbed explores machine-learning techniques and evaluates algorithmic approaches for time-critical predictive maintenance. This knowledge leads to actionable insight enabling companies to move away from traditional preventative maintenance to predictive maintenance, which minimizes unplanned downtime and optimizes system operation. This would ultimately help manufacturers increase availability, improve energy efficiency and extend the lifespan of high-volume CNC manufacturing production systems.

About a week ago, Stanford University researchers posted online a study on the latest dystopian AI: They'd made a machine learning algorithm that essentially works as gaydar. After training the algorithm with tens of thousands of photographs from a dating site, the algorithm could, for example, guess if a white man in a photograph was gay with 81 percent accuracy. They wanted to protect gay people. "[Our] findings expose a threat to the privacy and safety of gay men and women," wrote Michal Kosinski and Yilun Wang in the paper. They built the bomb so they could alert the public about its dangers.

Craig Federighi, Apple's senior vice president of software engineering says there are two things you can do to stop nefarious actors from forcing you into FaceID. According to Federighi, "If you don't stare at the phone, it won't unlock," & "If you grip the buttons on both sides of the phone when you hand it over, it will temporarily disable Face ID." Clearly, iPhone X owners will have to practice their squeezing techniques. It would be painful and costly to be held up and discover that you were squeezing it all wrong. The ACLU & the EFF recently sued the DHS for searching the phones and laptops of 11 plaintiffs at the US border without a warrant. The group of plaintiffs includes 10 US citizens and one lawful permanent resident, several of whom are Muslims or people of color.

Stanford's review board approved Kosinski and Wang's study. "The vast, vast, vast majority of what we call'big data' research does not fall under the purview of federal regulations," says Metcalf. Take a recent example: Last month, researchers affiliated with Stony Brook University and several major internet companies released a free app, a machine learning algorithm that guesses ethnicity and nationality from a name to about 80 percent accuracy. The group also went through an ethics review at the company that provided training list of names, although Metcalf says that an evaluation at a private company is the "weakest level of review that they could do."

One of the activities of AI-Ethics.com is to monitor and report on the work of all groups that are writing draft principles to govern the future legal regulation of Artificial Intelligence. Many have been proposed to date. Click here to go to the AI-Ethics Draft Principles page. If you know of a group that has articulated draft principles not reported on our page, please let me know. At this point all of the proposed principles are works in progress.

For months, Waymo had been seeking to obtain a 2016 due diligence report that Uber had completed prior to obtaining Levandowski's company. Waymo hoped it would shed light on what Uber knew about Levandowski's downloads, and when Uber knew them.

Earlier this week, Apple unveiled its new flagship smartphone, the iPhone X. Its marquis feature is a front-facing array of sensors it calls the TrueDepth camera, designed to recognize and track a user's face. It replaces the fingerprint scanner as the biometric method for unlocking the phone. The iPhone X isn't the first device to include this feature. Windows Hello (which is mainly for laptops and tablets), and the Samsung Galaxy S8 also incorporate a facial recognition login, though the latter is based around an iris scanner rather than a whole face match. But Apple has especially made the security of its iPhone a critical aspect of its marketing, refusing in 2016 to unlock the San Bernardino gunman's iPhone.