Alexa, Siri and Cortana may sound like the top three hipster baby names in 2021, but they are actually Amazon, Apple and Microsoft's virtual assistants. In recent years, we have experienced a boom in speech recognition tools that understand what we are saying. And soon they could also understand how we are feeling. The list of companies working on the development of emotion recognition technology is growing exponentially, and investors appear to be excited when it comes to emotionally intelligent tech. The industry is undoubtedly booming, with estimates predicting that the global emotional intelligence market will grow to $64m by 2027. The most common form of emotion detection software uses cameras to record and analyse facial expressions, body movements and gestures to detect how people are feeling.

LONDON – A new law designed to regulate artificial intelligence in Europe could end up costing the EU economy 31 billion euros ($36 billion) over the next five years, according to a report from Washington-based think tank the Center for Data Innovation released on Sunday. The Artificial Intelligence Act -- a proposed law put forward by the European Commission, the executive arm of the EU -- will be the "world's most restrictive regulation of AI," according to the center. "It will not only limit AI development and use in Europe but impose significant costs on EU businesses and consumers," the organization said in the report. The commission said it disagrees with the findings of the report and that they appear to be flawed. The Center for Data Innovation argues that a small or mid-sized enterprise with a turnover of 10 million euros would face compliance costs of up to 400,000 euros if it deployed a high-risk AI system.

It is often argued that one goal of explaining automated decision systems (ADS) is to facilitate positive perceptions (e.g., fairness or trustworthiness) of users towards such systems. This viewpoint, however, makes the implicit assumption that a given ADS is fair and trustworthy, to begin with. If the ADS issues unfair outcomes, then one might expect that explanations regarding the system's workings will reveal its shortcomings and, hence, lead to a decrease in fairness perceptions. Consequently, we suggest that it is more meaningful to evaluate explanations against their effectiveness in enabling people to appropriately assess the quality (e.g., fairness) of an associated ADS. We argue that for an effective explanation, perceptions of fairness should increase if and only if the underlying ADS is fair. In this in-progress work, we introduce the desideratum of appropriate fairness perceptions, propose a novel study design for evaluating it, and outline next steps towards a comprehensive experiment.

The question whether an artificial intelligence ("AI") system can be named as an inventor in a patent application has obvious implications for the life science community, where AI's presence is now well established and growing. For example, AI is currently used to predict biological targets of prospective drug molecules, identify candidates for drug design, decode genetic material of viruses in the context of vaccine development, determine three-dimensional structures of proteins, including their folding form, and many more potential therapeutic applications. In a landmark decision issued on July 30, 2021, an Australian court declared that an AI system called DABUS can be legally recognized as an inventor on a patent application. It came just days after the Intellectual Property Commission of South Africa granted a patent recognizing DABUS as an inventor. These decisions, as well as at least one other pending case in the U.S. concerning similar issues, have generated excitement and debate in the life sciences community about AI-conceived inventions.

On a typical day, lawyers would research cases, draft briefs, or advise clients. However, thanks to artificial intelligence (AI) and machine learning (ML), robots are now able to do these complex tasks. The advancement of AI and ML is already taking over jobs that were until now reserved for professionals and people with expertise, such as lawyers. However, things may soon change. According to a report published in Social Science Research Network, researchers say they have found a way to predict summary judgment outcomes from the text of the parties' briefs. They have used linguistic analysis and ML techniques to do that.

Lady Gaga sings'I'm on the edge of glory and I'm hanging on a moment of truth'. Until now, the longstanding crusade to allow inventions generated by the AI machine DABUS to be patentable under existing national patent laws across different jurisdictions had not had much success. Lawyers with the "Artificial Inventor Project" had filed patent applications around the world for DABUS' 'inventions' but received a steady stream of rejections from national IP offices and courts (for instance see our Lens posts on refusals by the UKIPO, UK High Court, EPO and USPTO). Surprisingly, DABUS has had better results in recent weeks in respect of its South African and Australian applications. Is this the edge of glory?

The world of technology, which often breaks down barriers, can significantly promote more integration of people with disabilities into social and work contexts. In particular, artificial intelligence solutions may allow the removal of accessibility barriers. For those who develop technology, it is essential not only to think about usability but increasingly about accessibility. Especially those who deal with AI have the opportunity to create systems and solutions that can really break down barriers for people with disabilities of various kinds. This opens up an important debate that must involve both the world of technology and all those involved in ethical issues.

Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class imbalance, which manifests in many real-life applications, and mitigate discrimination mainly because they "reject" minority instances at large due to their inability to effectively learn all classes. In this work, we propose \ours, an online fairness-aware approach that maintains a valid and fair classifier over the stream. \ours~is an online boosting approach that changes the training distribution in an online fashion by monitoring stream's class imbalance and tweaks its decision boundary to mitigate discriminatory outcomes over the stream. Experiments on 8 real-world and 1 synthetic datasets from different domains with varying class imbalance demonstrate the superiority of our method over state-of-the-art fairness-aware stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%] in kappa and [89.4\%-96.6\%] in statistical parity (fairness).

As the regulatory environment continues to evolve at pace, leading organizations are addressing AI ethics and governance proactively rather than waiting for requirements to be enforced upon them. Through the course of 2020 we've seen AI deployed to help organizations better anticipate COVID-19 impact across the globe and industry sectors, so that they can respond to it with greater resiliency. In 2020, we have also seen revitalized focus on the role technology and AI plays across the environmental, social, and governance (ESG) landscape. This includes AI use cases and applications in healthcare, education, law enforcement, and financial services among others. Relative expansion of AI-driven use cases has highlighted both the benefits and the potential risks of AI -- notably the issue of trust in technology.

The company did not explain why Barriga, McCree and LeCraft are no longer employed, but several current employees confirmed to The Washington Post that all three were fired. McCree and LeCraft were photographed in a hotel room at the company's 2013 convention, BlizzCon, alongside Alex Afrasiabi, who was terminated in June of 2020 over multiple allegations. The hotel room in the photograph was referenced by Blizzard employees as the "Cosby Suite," according to the lawsuit, in reference to former comedian Bill Cosby, whose conviction for sexual assault was recently overturned.