LONDON (AP) — Risky uses of artificial intelligence that threaten people’s safety or rights such as live facial scanning should be banned or tightly controlled, European Union officials said Wednesday as they outlined an ambitious package of proposed regulations to rein in the rapidly expanding technology. The draft regulations from the EU's executive commission include rules for applications deemed high risk such as AI systems to filter out school, job or loan applicants. They would also ban artificial intelligence outright in a few cases considered too risky, such as government “social scoring” systems that judge people based on their behavior. The proposals are the 27-nation bloc’s latest move to maintain its role as the world’s standard-bearer for technology regulation, as it tries to keep up with the world's two big tech superpowers, the U.S. and China. EU officials say they are taking a four-level “risk-based approach” that seeks to balance important rights such as privacy against the need to encourage innovation.

Who you are, what you have experienced, and how you think inspires us to be innovative and bold. Loyal is an equal opportunity employer. We hire great people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger. We welcome the unique contributions that you can bring in terms of your education, opinions, culture, ethnicity, race, ancestry, sex, gender identity and expression, national origin, citizenship, marital status, age, languages spoken, veteran status, color, religion, disability, sexual orientation, and beliefs. We consider qualified applicants regardless of criminal histories, consistent with legal requirements. Further, consistent with applicable federal and state law, Loyal provides reasonable accommodations when requested by qualified applicants or employees with disabilities, unless doing so would cause an undue hardship. Loyal's policy regarding requests for reasonable accommodation applies to all aspects of employment, including the application process.

On 30 July 2021, the Federal Court of Australia decided that AI systems can be inventors. In a word-first determination of Thaler v Commissioner of Patents,{[2021] FCA 879, ('Thaler')}, the Honourable Justice Beach found that AI systems can be the inventors on a patent application under Australian patent law. The decision has been appealed to the Full Bench of the Federal Court, which may decide to overrule it. For now, however, the decision is binding in Australia. Read on to find out what a patent is and an overview of the decision.

The last module of "The People Analytics Journey" – the introductory course for HR professionals that I taught in Tel Aviv – was dedicated to the future of People Analytics. We discuss the question – Will People Analysts always be human?, and I offered some practice guideline in Procurement and Ethics. As HR practitioners still lag in their understanding of analytics and AI, I think that this module illustrated the path needed to close the gap, without the math and the coding, of course. In part 1 of the article AI for HR – Five themes that you must understand, I emphasized that the realm of work changes, as every stage of the employee lifecycle is affected by AI. To face the difficulties that we encounter, I called HR leaders to start by understanding five themes: 1) What AI is – or isn't?

Selection for third-cycle studies is based on the student's potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Consideration will be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme. Technology and society is a third-cycle subject that encompasses multidisciplinary and interdisciplinary studies of technology's role, interplay and importance in different sectors of society. The position is linked to a highly interdisciplinary research project that explores how AI transparency relates to consumer trust.

Technology is something you can't escape, no matter how hard you try. Even in your personal life, you could try your hardest to live like it's 1985, but sooner or later, you're going to have to play along and join a Zoom call. The same thing applies to legal practice. After decades of clinging on to the old-fashioned approach, law firms are finally getting their groove on and adopting new technologies in their day-to-day processes. It would be an understatement to say that the impact has been game-changing, the legal industry is no longer the same as it was, even a decade ago.

Computer scientists and researchers are increasingly investigating techniques that can create backdoors in machine-learning (ML) models -- first to understand the potential threat, but also as an anti-copying protection to identify when ML implementations have been used without permission. Originally known as BadNets, backdoored neural networks represent both a threat and a promise of creating unique watermarks to protect the intellectual property of ML models, researchers say. The training technique aims to produce a specially crafted output, or watermark, if a neural network is given a particular trigger as an input: A specific pattern of shapes, for example, could trigger a visual recognition system, while a particular audio sequence could trigger a speech recognition system. Originally, the research into backdooring neural networks was meant as a warning to researchers to make their ML models more robust and to allow them to detect such manipulations. But now research has pivoted to using the technique to detect when a machine-learning model has been copied, says Sofiane Lounici, a data engineer and machine-learning specialist at SAP Labs France.

The Dubai International Financial Center launched a license for artificial intelligence (AI) and coding today. The designated economic zone's license was launched in cooperation with the United Arab Emirates Artificial Intelligence Office to attract AI companies to the UAE. Licensed firms will receive access to the Dubai International Financial Center's Innovation Hub, where several fintech companies operate, the center said in a press release. The Dubai International Financial Center is a financial hub with an English common law-based legal framework that is separate from the rest of the Emirates. The center is home to more than 2,500 companies.

The Concordance Index (C-index) is a commonly used metric in Survival Analysis to evaluate how good a prediction model is. This paper proposes a decomposition of the C-Index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition allows a more fine-grained analysis of the pros and cons of survival prediction methods. The utility of the decomposition is demonstrated using three benchmark survival analysis models (Cox Proportional Hazard, Random Survival Forest, and Deep Adversarial Time-to-Event Network) together with a new variational generative neural-network-based method (SurVED), which is also proposed in this paper. The demonstration is done on four publicly available datasets with varying censoring levels. The analysis with the C-index decomposition shows that all methods essentially perform equally well when the censoring level is high because of the dominance of the term measuring the ranking of events versus censored cases. In contrast, some methods deteriorate when the censoring level decreases because they do not rank the events versus other events well.

In this paper we look at popular fairness methods that use causal counterfactuals. These methods capture the intuitive notion that a prediction is fair if it coincides with the prediction that would have been made if someone's race, gender or religion were counterfactually different. In order to achieve this, we must have causal models that are able to capture what someone would be like if we were to counterfactually change these traits. However, we argue that any model that can do this must lie outside the particularly well behaved class that is commonly considered in the fairness literature. This is because in fairness settings, models in this class entail a particularly strong causal assumption, normally only seen in a randomised controlled trial. We argue that in general this is unlikely to hold. Furthermore, we show in many cases it can be explicitly rejected due to the fact that samples are selected from a wider population. We show this creates difficulties for counterfactual fairness as well as for the application of more general causal fairness methods.