The safety driver behind the wheel of a self-driving Uber that struck and killed a woman in 2018 has been charged with a crime. Prosecutors in Maricopa County, Arizona, Tuesday said the driver, Rafaela Vasquez, has been indicted for criminal negligence. But Uber, her employer and the company that built the automated system involved in the fatal collision, won't face charges. The attorney for neighboring Yavapai County declined to prosecute Uber last year, writing in a letter that the office found "no basis for criminal liability." Yavapai County attorney Sheila Polk declined to elaborate on her decision.

The interest in Artificial Intelligence (AI) has grown so much in the past few months, with news alerts and updates about how Artificial Intelligence is being used in almost every area of life. Last year, Information Week published a "10 Prime Industries for AI Applications" and it was interesting to read how much AI is already being used in the world. There are applications for AI in business, education, the legal field, healthcare, manufacturing, military, politics, science; and the use of AI continues to evolve at a rapid pace. Approximately 77% of people are using AI every day while only 33% of consumers think that they are. Think about some of your daily activities when it comes to communication, transportation, or shopping, for a few examples.

Intellectual Property rewards people for creativity and innovation. It is crucial to the proper functioning of an innovative economy. The UK is voted one of the best IP environments in the world. To keep it that way we are keen to look ahead to the challenges that new technologies bring. We need to make sure the UK's IP environment is adapted to accommodate them.

Court documents released in August revealed that Swiss tax officials are investigating art dealer and freeport magnate Yves Bouvier for allegedly concealing CHF 330 million in profits. The Swiss authorities believe that Bouvier used a fictitious residence in Singapore to evade taxes in his home country, and confiscated one of Bouvier's properties, reportedly worth CHF 4.5 million, as a pledge while they continue investigating his finances. The investigation, however, was nearly derailed in its early stages due to a single vulnerable tax official. An escort girl known only as Sarah has testified that in September 2017, Yves Bouvier sent her to a conference to seduce a key official with Switzerland's Federal Tax Administration. Sarah's honeypot adventure took place mere months after Swiss tax officials had begun looking into Bouvier's finances.

Less than 20% of Europeans believe that current laws "efficiently regulate" artificial intelligence, and 56% have low trust in authorities to exert effective control over the technology, according to a new survey from the European Consumer Organisation (BEUC). The findings have important implications for the governance and design of AI-powered public services, emphasising the need to address citizens' fears over transparency, accountability, equity in decision-making, and the management of personal data. The BEUC surveyed 11,500 consumers in nine European countries: Belgium, Denmark, France, Germany, Italy, Poland, Portugal, Spain and Sweden. It found that while a large majority of respondents feel that artificial intelligence (AI) can be useful, most don't trust the technology and feel that current regulations do not protect them from the harms it can cause. It also found that 66% of respondents from Belgium, Italy, Portugal and Spain agree that AI can be hazardous and should be banned by authorities.

There are well-documented examples of AI systems making decisions that affect protected classes, such as housing assistance or unemployment benefits. AI can be used to screen resumes; banks apply AI models to grant individual consumers credit and set interest rates for them. Many small decisions, taken together, can have large effects, such as: AI-driven price discrimination could lead to certain groups in a society consistently paying more. But are there AI applications today that affect everyone, no matter their "class"? As I mentioned earlier, we are shifting our AI Ethics courses to more practical, useful techniques.

Earlier this year, the independent research organisation of which I am the Director, London-based Ada Lovelace Institute, hosted a panel at the world's largest AI conference, CogX, called The Ethics Panel to End All Ethics Panels. The title referenced both a tongue-in-cheek effort at self-promotion, and a very real need to put to bed the seemingly endless offering of panels, think-pieces, and government reports preoccupied with ruminating on the abstract ethical questions posed by AI and new data-driven technologies. We had grown impatient with conceptual debates and high-level principles. And we were not alone. It supersedes the two waves that came before it: the first wave, defined by principles and dominated by philosophers, and the second wave, led by computer scientists and geared towards technical fixes.

Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems. In this introduction to algorithmic fairness in education, we draw parallels to prior literature on educational access, bias, and discrimination, and we examine core components of algorithmic systems (measurement, model learning, and action) to identify sources of bias and discrimination in the process of developing and deploying these systems. Statistical, similarity-based, and causal notions of fairness are reviewed and contrasted in the way they apply in educational contexts. Recommendations for policy makers and developers of educational technology offer guidance for how to promote algorithmic fairness in education.

Memes are graphics and text overlapped so that together they present concepts that become dubious if one of them is absent. It is spread mostly on social media platforms, in the form of jokes, sarcasm, motivating, etc. After the success of BERT in Natural Language Processing (NLP), researchers inclined to Visual-Linguistic (VL) multimodal problems like memes classification, image captioning, Visual Question Answering (VQA), and many more. Unfortunately, many memes get uploaded each day on social media platforms that need automatic censoring to curb misinformation and hate. Recently, this issue has attracted the attention of researchers and practitioners. State-of-the-art methods that performed significantly on other VL dataset, tends to fail on memes classification. In this context, this work aims to conduct a comprehensive study on memes classification, generally on the VL multimodal problems and cutting edge solutions. We propose a generalized framework for VL problems. We cover the early and next-generation works on VL problems. Finally, we identify and articulate several open research issues and challenges. This is the first study that presents the generalized view of the advanced classification techniques concerning memes classification to the best of our knowledge. We believe this study presents a clear road-map for the Machine Learning (ML) research community to implement and enhance memes classification techniques.

In 2020, artificial intelligence (AI) is no longer seen as something futuristic: it is a part of our everyday lives. AI appears to be on everyone's lips, and is clearly a growing force in the technology industry. While AI's proliferation in mainstream society is a new phenomenon, it is not a new concept. For businesses, practical AI applications can be applied in so many ways based on organizational needs and business intelligence (BI) insights derived from collected data. The technology can deliver a substantial qualitative change to business organizations and create new opportunities for company growth.