Health care services in Africa are under-resourced & overused. There are only 20 pediatric radiologists in Africa, where pneumonia is the #1 cause of death in children under 5. Covid-19, tuberculosis and cancer are an increased burden on doctors globally and is another threat to our already fragile healthcare system. Misdiagnosis is common and human error can result in increased medical legal exposure to doctors. RADIFY AI for mammography assists with early detection of breast cancer. Radify AI for ultrasound is a point of care solution for detection of chest & breast diseases.

Tens of thousands of times a year, a technician places a drop of blood on a slide and peers at it under a microscope, searching for malaria parasites. Making a definitive diagnosis requires the technician to look at up to 300 different fields of view over roughly half an hour. This process is repeated over and over, day after day, on every continent except Antarctica. It's tedious work, but it saves lives. Malaria parasites infect over 200 million people and kill 400,000 every year, mostly children in Africa. Trained and experienced malaria microscopists are rare, however.

As AI systems are integrated into high stakes social domains, researchers now examine how to design and operate them in a safe and ethical manner. However, the criteria for identifying and diagnosing safety risks in complex social contexts remain unclear and contested. In this paper, we examine the vagueness in debates about the safety and ethical behavior of AI systems. We show how this vagueness cannot be resolved through mathematical formalism alone, instead requiring deliberation about the politics of development as well as the context of deployment. Drawing from a new sociotechnical lexicon, we redefine vagueness in terms of distinct design challenges at key stages in AI system development. The resulting framework of Hard Choices in Artificial Intelligence (HCAI) empowers developers by 1) identifying points of overlap between design decisions and major sociotechnical challenges; 2) motivating the creation of stakeholder feedback channels so that safety issues can be exhaustively addressed. As such, HCAI contributes to a timely debate about the status of AI development in democratic societies, arguing that deliberation should be the goal of AI Safety, not just the procedure by which it is ensured.

Recovery of support of a sparse vector from simple measurements is a widely studied problem, considered under the frameworks of compressed sensing, 1-bit compressed sensing, and more general single index models. We consider generalizations of this problem: mixtures of linear regressions, and mixtures of linear classifiers, where the goal is to recover supports of multiple sparse vectors using only a small number of possibly noisy linear, and 1-bit measurements respectively. The key challenge is that the measurements from different vectors are randomly mixed. Both of these problems were also extensively studied recently. In mixtures of linear classifiers, the observations correspond to the side of queried hyperplane a random unknown vector lies in, whereas in mixtures of linear regressions we observe the projection of a random unknown vector on the queried hyperplane. The primary step in recovering the unknown vectors from the mixture is to first identify the support of all the individual component vectors. In this work, we study the number of measurements sufficient for recovering the supports of all the component vectors in a mixture in both these models. We provide algorithms that use a number of measurements polynomial in $k, \log n$ and quasi-polynomial in $\ell$, to recover the support of all the $\ell$ unknown vectors in the mixture with high probability when each individual component is a $k$-sparse $n$-dimensional vector.

The EU is facing a backlash over new AI rules that allow for limited use of facial recognition by authorities -- with opponents warning the carveouts could usher in a new age of biometric surveillance. A coalition of digital rights and consumer protection groups across the globe, including Latin America, Africa and Asia are calling for a global ban on biometric recognition technologies that enable mass and discriminatory surveillance by both governments and corporations. In an open letter, 170 signatories in 55 countries argue that the use of technologies like facial recognition in public places goes against human rights and civil liberties. "It shows that organizations, groups, people, activists, technologists around the world who are concerned with human rights, agree to this call," said Daniel Leufer of U.S. digital rights group Access Now, which co-authored the letter. The use of facial recognition technology is becoming widespread.

AI drone may have'hunted down' and killed soldiers in Libya without human input By Charles Q. Choi - Live Science Contributor - June 3, 2021 KARGU a Rotary Wing Attack Drone Loitering Munition System A UN report suggests that at least one autonomous drone operated by artificial intelligence (AI) may have killed people for the first time last year in Libya, without any humans consulted prior to the attack, according to a U.N. report. According to a March report from the U.N. Panel of Experts on Libya, lethal autonomous aircraft may have "hunted down and remotely engaged" soldiers and convoys fighting for Libyan general Khalifa Haftar. It's not clear who exactly deployed these killer robots, though remnants of one such machine found in Libya came from the Kargu-2 drone, which is made by Turkish military contractor STM. Landmines are essentially simple autonomous weapons -- you step on them and they blow up," Zachary Kallenborn, a research affiliate with the National Consortium for the ...

Despite their popularity with kids, tablets and other connected devices are built on top of systems that weren't designed for them to easily understand or navigate. But adapting algorithms to interact with a child isn't without its complications--as no one child is exactly like another. Most recognition algorithms look for patterns and consistency to successfully identify objects. But kids are notoriously inconsistent. In this episode, we examine the relationship AI has with kids. This episode was reported and produced by Jennifer Strong, Anthony Green and Tanya Basu with Emma Cillekens. Jennifer: It wasn't long ago that playing hopscotch, board games or hosting tea parties with dolls was the norm for kids.... But... we've seen hopscotch turn to TicToc... board games become video games... and dolls at tea parties... do more than just talk back This is my digital makeover.. I insert my own Ipad and open my app .. and the mirror lights up..

The potential value added of artificial intelligence (AI) to businesses is undisputed, yet research confirms that most companies still struggle to capitalize on the technology. In a recent panel hosted by YPO member and Managing Director of Techstars Vijay Tirathrai and Jean-Philippe Linteau, Consul General of Canada in Dubai and the Northern Emirates, industry leaders from Canada and the Middle East shared insights on how organizations can leverage AI while mitigating risks. According to the International Data Corporation's latest release, worldwide revenues for the AI market are forecast to grow 16.4% year-over-year, reaching USD554.3 billion by 2024. Along with the U.S. and China, Canada is positioned to gain the most from this growth. "Canada has a thriving AI ecosystem, with world-leading research centers that have evolved into major hubs of AI, including Canada's supercluster project in Montreal, Scale AI," says Linteau. "Canada is now home to more than 800 AI companies, including more than 45 global tech multinationals, more than 60 investment groups, and 40-plus accelerators and incubators that focus on AI."

Sexism has become an increasingly major problem on social networks during the last years. The first shared task on sEXism Identification in Social neTworks (EXIST) at IberLEF 2021 is an international competition in the field of Natural Language Processing (NLP) with the aim to automatically identify sexism in social media content by applying machine learning methods. Thereby sexism detection is formulated as a coarse (binary) classification problem and a fine-grained classification task that distinguishes multiple types of sexist content (e.g., dominance, stereotyping, and objectification). This paper presents the contribution of the AIT_FHSTP team at the EXIST2021 benchmark for both tasks. To solve the tasks we applied two multilingual transformer models, one based on multilingual BERT and one based on XLM-R. Our approach uses two different strategies to adapt the transformers to the detection of sexist content: first, unsupervised pre-training with additional data and second, supervised fine-tuning with additional and augmented data. For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets and fine-tuning on the provided dataset. The best run for the binary classification (task 1) achieves a macro F1-score of 0.7752 and scores 5th rank in the benchmark; for the multiclass classification (task 2) our best submission scores 6th rank with a macro F1-score of 0.5589.

This article describes an action rule induction algorithm based on a sequential covering approach. Two variants of the algorithm are presented. The algorithm allows the action rule induction from a source and a target decision class point of view. The application of rule quality measures enables the induction of action rules that meet various quality criteria. The article also presents a method for recommendation induction. The recommendations indicate the actions to be taken to move a given test example, representing the source class, to the target one. The recommendation method is based on a set of induced action rules. The experimental part of the article presents the results of the algorithm operation on sixteen data sets. As a result of the conducted research the Ac-Rules package was made available.