US Army scientists have developed a novel tool that can help soldiers detect deepfakes that pose threat to national security. The advance could lead to a mobile software that warns people when fake videos are played on the phone. Deepfakes are hyper-realistic video content made using artificial intelligence tools that falsely depicts individuals saying or doing something, explained Suya You and Shuowen (Sean) Hu from the Army Research Laboratory in the US. The growing number of these fake videos in circulation can be harmful to society – from the creation of non-consensual explicit content to doctored media by foreign adversaries that are used in disinformation campaigns. According to the scientists, while there were close to 8,000 of these deepfake video clips online at the beginning of 2019, in just about nine months, this number nearly doubled to about 15,000.

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.

In what could be a harbinger of the future regulation of artificial intelligence (AI) in the United States, the European Commission published its recent proposal for regulation of AI systems. The proposal is part of the European Commission's larger European strategy for data, which seeks to "defend and promote European values and rights in how we design, make and deploy technology in the economy." To this end, the proposed regulation attempts to address the potential risks that AI systems pose to the health, safety, and fundamental rights of Europeans caused by AI systems. Under the proposed regulation, AI systems presenting the least risk would be subject to minimal disclosure requirements, while at the other end of the spectrum AI systems that exploit human vulnerabilities and government-administered biometric surveillance systems are prohibited outright except under certain circumstances. In the middle, "high-risk" AI systems would be subject to detailed compliance reviews.

On 28 April 2021, the UK announced the forthcoming creation of regulations for the use of autonomous vehicles. Operating at reduced and limited speeds, these cars could be allowed on British roads by the end of the year. In France, legislation in this area was the subject of a national strategy for the development of automated road mobility, published last December. The marketing of vehicles with the first level 3 autonomous driving functionalities is progressing. As of January 1, 2021, a UN regulation allows manufacturers to offer for sale individual vehicles with lane-keeping capabilities at a maximum speed of 60 kilometers per hour.

AI spy: On Wednesday, France's Prime Minister Jean Castex said the government plans to submit a bill to parliament seeking permanent authority to order telecoms firms to monitor not just telephone data but also the full URLs of specific webpages their users visit in real time. Government algorithms would alert intelligence officials when certain criteria are met, such as an internet user visiting a specific sequence of pages. French Interior Minister Gerard Darmanin said intelligence officials would need approval from him, the prime minister and an outside agency to unmask a person flagged for his or her browsing. One portion of the bill would allow French intelligence officials to use older intelligence data, including data the government isn't currently allowed to retain, to train AI systems to look for unforeseen patterns and develop new tools. An interior ministry official said such data would be anonymized, though privacy experts say anonymizing data so it can't be later reattributed is difficult.

Big data has evolved from a technology buzzword into a real-world solution that helps companies and governments analyze data, extract the meaningful statistics, and apply it into their specific business needs. It's not so much the realization that this information is collected, but what can be effectively done with it. There is a use for big data in pretty much everything we do, with the economic forecasts proving to be no different. The emergence of big data and artificial intelligence (AI) is no big secret in the private sector, but the use of it to improve the understanding of the economy and social issues – and better direct policy creation to make significant changes. Both the quality and quantity of economic activity data have increased in recent years, and there is no reason that should suddenly stop any time soon.

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation.

Our chosen model is based on PB with cardinal preferences, i.e., voters have numbers associated with projects Participatory budgeting (PB) is a democratic process that reflect their preferences. Cardinal preferences capture, where citizens jointly decide on how to allocate e.g., settings with approval ballots (only 0 and 1 public funds to indivisible projects. This paper are used), settings where voters can distribute points to focuses on PB processes where citizens may projects (where usually the sum of points is bounded), give additional money to projects they want to see and settings where these numbers accurately correspond funded. We introduce a formal framework for this to the utility of voters. Further, we allow for diversity kind of PB with donations. Our framework also constraints [Bredereck et al., 2018; Benabbou et al., 2019; allows for diversity constraints, meaning that each Yang et al., 2019; Chen et al., 2020a]: Each project belongs project belongs to one or more types, and there are to one or more types (based on classifications such as "youth lower and upper bounds on the number of projects and education" or "transport and mobility") and for each type of the same type that can be funded. We propose there is a minimum and maximum number of projects to be three general classes of methods for aggregating the funded. This can also model city-wide referenda where districts citizens' preferences in the presence of donations have their own "project quota".

AI has the potential to revolutionize many areas of healthcare. Radiology, dermatology, and ophthalmology are some of the areas most likely to be impacted in the near future, and they have received significant attention from the broader research community. But AI techniques are now also starting to be used in in vitro fertilization (IVF), in particular for selecting which embryos to transfer to the woman. The contribution of AI to IVF is potentially significant, but must be done carefully and transparently, as the ethical issues are significant, in part because this field involves creating new people. We first give a brief introduction to IVF and review the use of AI for embryo selection. We discuss concerns with the interpretation of the reported results from scientific and practical perspectives. We then consider the broader ethical issues involved. We discuss in detail the problems that result from the use of black-box methods in this context and advocate strongly for the use of interpretable models. Importantly, there have been no published trials of clinical effectiveness, a problem in both the AI and IVF communities, and we therefore argue that clinical implementation at this point would be premature. Finally, we discuss ways for the broader AI community to become involved to ensure scientifically sound and ethically responsible development of AI in IVF.

Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) framework for mitigating political biases in generated text. By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.