Ethics and Information Technology 20 (1): 1-3. 9 -- Page 2 of reference [8] 10 -- See: Winfield, A., Michael, K., Pitt, J., & Evers, V. (2019) Machine ethics: the design and governance of ethical AI and autonomous systems.

Proximie is being deployed across a number of NHS sites, to support the national efforts to fight COVID-19. Proximie uses a combination of machine learning, artificial intelligence and augmented reality, aimed to empower surgeons and clinicians, to virtually and practically interact with each other from anywhere. The platform, which was founded by Dr. Nadine Hachach-Haram FRCS (Plast), BEM, consultant plastic surgeon and head of clinical innovation at Guy's and St. Thomas' NHS Foundation Trust, is being used across a host of NHS sites, as the country battles the pandemic. From enabling self-isolating clinicians to remotely support colleagues on the front line, to virtually connecting MDTs for hand trauma and cancer management, so that every clinician can connect and collaborate off site during COVID-19, the platform is being applied in a number of different ways to support and amplify frontline clinicians. Using augmented reality, healthcare practitioners can remotely interact in a procedure or assessment from start to finish, and mentor a local clinician through a live operation, in a visually and intuitive way.

U.S. President Donald Trump's broadside against the World Health Organization is another blow to international institutions designed to help nations confront global crises -- and may leave countries even less prepared for the next one. Trump's move on Tuesday to suspend WHO funding amid a pandemic that has cost at least 130,000 lives is the latest salvo in a broader struggle between the U.S. and China over global leadership. Both countries are courting other nations and public opinion as they cover up their own shortcomings in the pandemic and position themselves for the post-virus world. China -- widely criticized for missteps early in the outbreak -- has ramped up efforts to send medical supplies to hard-hit nations, even as reports emerged that much of that gear was faulty or expired. The U.S., meanwhile, announced $300 million in aid to countries fighting the virus but rebuffed requests for the most essential gear while receiving donations from the governments of Egypt, Taiwan and Vietnam among others.

The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. Existing explanation methods generally create importance rankings in terms of pixels or pixel groups. However, the resulting explanations lack an optimal size, do not consider feature dependence and are only related to one class. Counterfactual explanation methods are considered promising to explain complex model decisions, since they are associated with a high degree of human interpretability. In this paper, SEDC is introduced as a model-agnostic instance-level explanation method for image classification to obtain visual counterfactual explanations. For a given image, SEDC searches a small set of segments that, in case of removal, alters the classification. As image classification tasks are typically multiclass problems, SEDC-T is proposed as an alternative method that allows specifying a target counterfactual class. We compare SEDC(-T) with popular feature importance methods such as LRP, LIME and SHAP, and we describe how the mentioned importance ranking issues are addressed. Moreover, concrete examples and experiments illustrate the potential of our approach (1) to obtain trust and insight, and (2) to obtain input for model improvement by explaining misclassifications.

We study the following metric distortion problem: there are two finite sets of points, V and C, that lie in the same metric space, and our goal is to choose a point in C whose total distance from the points in V is as small as possible. However, rather than having access to the underlying distance metric, we only know, for each point in V , a ranking of its distances to the points in C. We propose algorithms that choose a point in C using only these rankings as input and we provide bounds on their distortion (worst-case approximation ratio). A prominent motivation for this problem comes from voting theory, where V represents a set of voters, C represents a set of candidates, and the rankings correspond to ordinal preferences of the voters. A major conjecture in this framework is that the optimal deterministic algorithm has distortion 3. We resolve this conjecture by providing a polynomial-time algorithm that achieves distortion 3, matching a known lower bound. We do so by proving a novel lemma about matching rankings of candidates to candidates, which we refer to as the ranking-matching lemma. This lemma induces a family of novel algorithms, which may be of independent interest, and we show that a special algorithm in this family achieves distortion 3. We also provide more refined, parameterized, bounds using the notion of {\alpha}-decisiveness, which quantifies the extent to which a voter may prefer her top choice relative to all others. Finally, we introduce a new randomized algorithm with improved distortion compared to known results, and also provide improved lower bounds on the distortion of all deterministic and randomized algorithms.

In this study, we propose a machine-learning-based approach to identify the modal parameters of the output only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network becomes the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex cost function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach s good capability in blindly extracting modal information from system responses.

Chip maker Intel has been chosen to lead a new initiative led by the U.S. military's research wing, DARPA, aimed at improving cyber-defenses against deception attacks on machine learning models. Machine learning is a kind of artificial intelligence that allows systems to improve over time with new data and experiences. One of its most common use cases today is object recognition, such as taking a photo and describing what's in it. That can help those with impaired vision to know what's in a photo if they can't see it, for example, but it also can be used by other computers, such as autonomous vehicles, to identify what's on the road. But deception attacks, although rare, can meddle with machine learning algorithms.

"I would love to see a future where looking inside the body becomes as routine as a blood pressure cuff measurement," says Charles Cadieu '04, MEng '05. As president of the medical technology startup Caption Health, he sees that future in reach--with the help of artificial intelligence. Cadieu still remembers the "lightbulb moment" during his postdoctoral research at MIT when he realized that the field of AI would never be the same. He was working in the lab of James DiCarlo (now the Peter de Florez Professor of Neuroscience) on neural networks--AI systems made up of deep-learning algorithms that emulate the dense networks of neurons in the brain. Until then, neural networks had been unable to perform even simple visual tasks that the brain handles with ease.

Astronauts rely on highly-engineered and sophisticated pieces of equipment to survive in space, and none are more essential than the parts which form their suit. Now, the European Space Agency (ESA) has revealed a concept glove that makes the protective equipment smarter and more interactive for the wearer. It will feature a range-finding laser, a display screen to show the suit's status and gesture control technology allowing people to control machines, such as the martian drone or lunar rover, with a flick of the wrist. The European Space Agency (ESA) has revealed a concept glove that makes the protective equipment smarter and more interactive for the wearer. It will feature a range-finding laser, a display screen to show the suit's status and gesture control technology allowing people to control machines, such as the martian drone or lunar rover, with a flick of the wrist The glove created by the European Space Agency has been made as part of a project from French company Comex and designer Agatha Medioni.

The tech industry is entering a new age, one in which innovation has to be done responsibly. "It's very novel," says Michael Kearns, a professor at the University of Pennsylvania specialising in machine learning and AI. "The tech industry to date has largely been amoral (but not immoral). Now we're seeing the need to deliberately consider ethical issues throughout the entire tech development pipeline. I do think this is a new era."