A leading research centre has called for new laws to restrict the use of emotion-detecting tech. The AI Now Institute says the field is "built on markedly shaky foundations". Despite this, systems are on sale to help vet job seekers, test criminal suspects for signs of deception, and set insurance prices. It wants such software to be banned from use in important decisions that affect people's lives and/or determine their access to opportunities. The US-based body has found support in the UK from the founder of a company developing its own emotional-response technologies - but it cautioned that any restrictions would need to be nuanced enough not to hamper all work being done in the area.

This article is an excerpt from the Pearson Addison-Wesley book "Pragmatic AI" by Noah Gift. Reprinted here with permission from Pearson and 2019. What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage. Recommendation engines can be gamed because they amplify the effects of thought bubbles.

Ferguson is a professor of law at the University of the District of Columbia School of Law and the author of The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. Can a computer predict violence? In Chicago, Illinois, an algorithm rates every person arrested with a numerical threat score from 1 to 500-plus. The process has been going on for four years, and almost 400,000 Chicago citizens now have an official police risk score. This algorithm -- still secret and publicly unaccountable -- shapes policing strategy, the use of force, and threatens to alter suspicion on the streets.

In recent years, interest in monitoring air quality has been growing. Traditional environmental monitoring stations are very expensive, both to acquire and to maintain, therefore their deployment is generally very sparse. This is a problem when trying to generate air quality maps with a fine spatial resolution. Given the general interest in air quality monitoring, low-cost air quality sensors have become an active area of research and development. Low-cost air quality sensors can be deployed at a finer level of granularity than traditional monitoring stations. Furthermore, they can be portable and mobile. Low-cost air quality sensors, however, present some challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Some promising machine learning approaches can help us obtain highly accurate measurements with low-cost air quality sensors. In this article, we present low-cost sensor technologies, and we survey and assess machine learning-based calibration techniques for their calibration. We conclude by presenting open questions and directions for future research.

The potential for advances in information-age technologies to undermine nuclear deterrence and influence the potential for nuclear escalation represents a critical question for international politics. One challenge is that uncertainty about the trajectory of technologies such as autonomous systems and artificial intelligence (AI) makes assessments difficult. This paper evaluates the relative impact of autonomous systems and artificial intelligence in three areas: nuclear command and control, nuclear delivery platforms and vehicles, and conventional applications of autonomous systems with consequences for nuclear stability. We argue that countries may be more likely to use risky forms of autonomy when they fear that their second-strike capabilities will be undermined. Additionally, the potential deployment of uninhabited, autonomous nuclear delivery platforms and vehicles could raise the prospect for accidents and miscalculation. Conventional military applications of autonomous systems could simultaneously influence nuclear force postures and first-strike stability in previously unanticipated ways. In particular, the need to fight at machine speed and the cognitive risk introduced by automation bias could increase the risk of unintended escalation. Finally, used properly, there should be many applications of more autonomous systems in nuclear operations that can increase reliability, reduce the risk of accidents, and buy more time for decision-makers in a crisis.

With the increasing growth of social media, people have started relying heavily on the information shared therein to form opinions and make decisions. While such a reliance is motivation for a variety of parties to promote information, it also makes people vulnerable to exploitation by slander, misinformation, terroristic and predatorial advances. In this work, we aim to understand and detect such attempts at persuasion. Existing works on detecting persuasion in text make use of lexical features for detecting persuasive tactics, without taking advantage of the possible structures inherent in the tactics used. We formulate the task as a multi-class classification problem and propose an unsupervised, domain-independent machine learning framework for detecting the type of persuasion used in text, which exploits the inherent sentence structure present in the different persuasion tactics. Our work shows promising results as compared to existing work.

Department of Computing Science, Ume a University 90187 Ume a, Sweden Abstract Abstract argumentation has emerged as a method for nonmonotonic reasoning that has gained tremendous traction in the symbolic artificial intelligence community. In the literature, the different approaches to abstract argumentation that were refined over the years are typically evaluated from a logics perspective; an analysis that is based on models of ideal, rational decision-making does not exist. In this paper, we close this gap by analyzing abstract argumentation from the perspective of the rational man paradigm in microeconomic theory. To assess under which conditions abstract argumentation-based choice functions can be considered economically rational, we define a new argumentation principle that ensures compliance with the rational man's reference independence property, which stipulates that a rational agent's preferences over two choice options should not be influenced by the absence or presence of additional options. We show that the argumentation semantics as proposed in Dung's classical paper, as well as all of a range of other semantics we evaluate do not fulfill this newly created principle. Consequently, we investigate how structural properties of argumentation frameworks impact the reference independence principle, and propose a restriction to argumentation expansions that allows all of the evaluated semantics to fulfill the requirements for economically rational argumentation-based choice. For this purpose, we define the rational man's expansion as a normal and noncyclic expansion. Finally, we put reference independence into the context of preference-based argumentation and show that for this argumentation variant, which explicitly model preferences, the rational man's expansion cannot ensure reference independence.

From disrupting elections to targeted ransomware to privacy regulations to deepfakes and malevolent AI, 141 cybersecurity predictions for 2020 did not exhaust the subject so here are additional 42 from senior cybersecurity executives. "2019 saw the cybersecurity industry start to explore AI-based solutions. In the coming months, cybercriminals will start to do the same, integrating AI and machine learning into their malware programs to bypass and infiltrate targeted systems. Current cybersecurity measures rely on'detection and response,' but as attackers begin to leverage AI to bypass existing solutions, companies will be left at a significant disadvantage against these seemingly undetectable campaigns. We could see AI-based malware become prominent in day-to-day attacks"--Guy Caspi, CEO, Deep Instinct "In 2020, we'll see an increasing number of cybercriminals use AI to scale their attacks. AI will open the door to mutating malware based on attackers using genetic algorithms that are ...

We're excited to release the AI Index 2019 Report, one of the most comprehensive studies about AI to date. Because AI touches so many aspects of society, the Index takes an interdisciplinary approach by design, analyzing and distilling patterns about AI's broad global impact on everything from national economies to job growth, research and public perception. The purpose of the project is to ground the discussion on AI in data, serving practitioners, industry leaders, policymakers and funders, the general public and the media that informs it. An independent initiative within Stanford University's Human-Centered Artificial Intelligence Institute, the report is in its third year and is the result of a collaborative effort led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry, in collaboration with more than 35 sponsoring partners and data contributors. The first two reports established the Index as the preeminent source of data about AI.

Washington State University researchers are creating the first-ever "IQ test" for artificial intelligence (AI) systems that would score systems on how well they learn and adapt to new, unknown environments. Diane Cook, Regents Professor and Huie-Rogers Chair Professor, and Larry Holder, professor in the School of Electrical Engineering and Computer Science, received a grant of just over $1 million from the Defense Advanced Research Projects Agency (DARPA) to create a framework to test the "intelligence" of AI systems. "Previously, research on measuring intelligence in AI systems has been mostly theoretical," Holder said. Holder and Cook will design a test that will grade AI systems based on the difficulty of problems that they can solve. Creating methods to rank problems on their difficulty will be one of the major parts of the research.