This study exploits the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing, to develop anticipatory multi-objective eco-routing strategies. For a robust application, several GHG costing approaches are examined. The predictive models for the link level traffic and emission states are developed using long short term memory deep network with exogenous predictors. It is found that anticipatory routing strategies outperformed the myopic strategies, regardless of the routing objective. Whether myopic or anticipatory, the multi-objective routing, with travel time and GHG minimization as objectives, outperformed the single objective routing strategies, causing a reduction in the average travel time (TT), average vehicle kilometre travelled (VKT), total GHG and total NOx by 17%, 21%, 18%, and 20%, respectively. Finally, the additional TT and VKT experienced by the vehicles in the network contributed adversely to the amount of GHG and NOx produced in the network.

The U.S. Congress should create a federal agency for artificial intelligence. In 1906, in response to shocking reports about the disgusting conditions in U.S. meat-packing facilities, Congress created the Food and Drug Administration (FDA) to ensure safe and sanitary food production. In 1934, in the wake of the worst stock market crash in U.S. history, Congress created the Securities and Exchange Commission (SEC) to regulate capital markets. In 1970, as the nation became increasingly alarmed about the deterioration of the natural environment, Congress created the Environmental Protection Agency (EPA) to ensure cleaner skies and waters. When an entire field begins to create a broad set of challenges for the public, demanding thoughtful regulation, a proven governmental approach is to create a federal agency focused specifically on engaging with and managing that field.

Members of Congress introduced a new bill on Thursday that would ban government use of biometric technology, including facial recognition tools. Pramila Jayapal and Ayanna Pressley announced the Facial Recognition and Biometric Technology Moratorium Act, which they said resulted from a growing body of research that "points to systematic inaccuracy and bias issues in biometric technologies which pose disproportionate risks to non-white individuals." The bill came just one day after the first documented instance of police mistakenly arresting a man due to facial recognition software. There has been long-standing, widespread concern about the use of facial recognition software from lawmakers, researchers rights groups and even the people behind the technology. Multiple studies over the past three years have repeatedly proven that the tool is still not accurate, especially for people with darker skin.

Since the first Industrial Revolution, mankind has been scared of future technologies. People were afraid of electricity. People were afraid of trains and cars. But it always took just one or two generations to get completely used to these innovations. It's true that most technologies caused harm in some ways, but the net outcome was usually good. This may be true for future technologies too, although there are serious ethical and philosophical reasons to be scared of some of them. Some of them shouldn't really scare us. And some of them are already shaping our world. Before we begin, I have to warn you: some of the things you will read in this story can be VERY controversial. I need you to approach this story with a very open mind, and acknowledge that the ideas I present here are just that, ideas. I hold no extreme or fixed views, nor do I claim to have the exact answers to ethical and philosophical questions. You may have completely different ideas, and that's totally fine. Cryonics may seem very sci-fi (to be fair, everything in this story does), but it already exists. There are companies that freeze you as soon as you die, so you can be brought back to life when technology and medicine will be advanced enough. Seriously, companies like this (I'm NOT affiliated to them).

When discussing the threats of artificial intelligence, the first thing that comes to mind are images of Skynet, The Matrix, and the robot apocalypse. The runner up is technological unemployment, the vision of a foreseeable future in which AI algorithms take over all jobs and push humans into a struggle for meaningless survival in a world where human labor is no longer needed. Whether any or both of those threats are real is hotly debated among scientists and thought leaders. But AI algorithms also pose more imminent threats that exist today, in ways that are less conspicuous and hardly understood. In her book, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, mathematician Cathy O'Neil explores how blindly trusting algorithms to make sensitive decisions can harm many people who are on the receiving end of those decisions.

On Tuesday, a number of AI researchers, ethicists, data scientists, and social scientists released a blog post arguing that academic researchers should stop pursuing research that endeavors to predict the likelihood that an individual will commit a criminal act, as based upon variables like crime statistics and facial scans. The blog post was authored by the Coalition for Critical Technology, who argued that the utilization of such algorithms perpetuates a cycle of prejudice against minorities. Many studies of the efficacy of face recognition and predictive policing algorithms find that the algorithms tend to judge minorities more harshly, which the authors of the blog post argue is due to the inequities in the criminal justice system. The justice system produces biased data, and therefore the algorithms trained on this data propagate those biases, the Coalition for Critical Technology argues. The coalition argues that the very notion of "criminality" is often based on race, and therefore research done on these technologies assumes the neutrality of the algorithms when in truth no such neutrality exists.

This article considers the law's response to the emergence of robots and artificial intelligence (AI), and whether they should be considered as legal persons and accordingly the bearers of legal rights. We analyse the regulatory issues raised by robot rights through three questions: (i) could robots be granted rights? On the question of whether we can recognise robot rights we examine how the law has treated different categories of legal persons and non-persons historically, finding that the concept of legal personhood is fluid and so arguably could be extended to include robots. However, as can be seen from the current debate in Intellectual Property (IP) law, AI and robots have not been recognised as the bearers of IP rights despite their ability to create and innovate, suggesting that the answer to the question of whether we will grant rights to robots is less certain. Finally, whether we should recognise rights for robots will depend on the intended purpose of regulatory reform.

Research shows how humans can shield machine learning from manipulation. Artificial intelligence and machine learning technologies are poised to supercharge productivity in the knowledge economy, transforming the future of work. Machine learning (ML) – technology in which algorithms "learn" from existing patterns in data to conduct statistically driven predictions and facilitate decisions – has been found in multiple contexts to reveal bias. Remember when Amazon.com came under fire for a hiring algorithm that revealed gender and racial bias? Such biases often result from slanted training data or skewed algorithms.

A Black man who was wrongfully arrested when facial recognition technology mistakenly identified him as a suspected shoplifter wants Detroit police to apologize -- and to end their use of the controversial technology. The complaint by Robert Williams is a rare challenge from someone who not only experienced an erroneous face recognition hit, but was able to discover that it was responsible for his subsequent legal troubles. The Wednesday complaint filed on Williams' behalf alleges that his Michigan driver license photo -- kept in a statewide image repository -- was incorrectly flagged as a likely match to a shoplifting suspect. Investigators had scanned grainy surveillance camera footage of an alleged 2018 theft inside a Shinola watch store in midtown Detroit, police records show. That led to what Williams describes as a humiliating January arrest in front of his wife and young daughters on their front lawn in the Detroit suburb of Farmington Hills.

The first tactic in the digital authoritarian toolkit is to establish information walls through fear, friction, or flooding. While employing traditional methods of repression and punishment to censor through fear, digital authoritarians also make it more difficult for citizens to access information through internet shutdowns, firewalls, and paywalls. In addition, digital dictators target traditional democratic values and freedoms by flooding the internet and other outlets for speech, press, and assembly. Inauthentic accounts ("bots"), deepfakes, and new tools of digital propaganda help states amplify narratives, build polarization, and increase "us versus them" divisions. With information walls, regimes can shape public opinion in newly-sophisticated ways by establishing state control over the messages their population can access--and the information they do not.