Artificial intelligence has had a huge impact on many industries in recent years and will continue to benefit them in the future. The pandemic-induced acceleration of technology adoption has led many sectors, both private and public to leverage AI for their advantage and growth. In the last few years, AI has enabled many innovations and driven the proliferation of technologies like IoT, robotics, analytics, and voice assistants. According to a report, AI topped the patent filings in 2020. This is not new, AI has been securing a large number of patents in the last few years.

AI, big data, and machine learning have witnessed exponential growth over the past few years. With the evolving technology, businesses realize the importance of adopting AI and big data in their operations. AI, big data, and machine learning create exciting new opportunities for companies and entrepreneurs. But this rapid adoption is also partnered with several complexities and risks, hence, comes the need for regulations. Regulators and policymakers find it difficult to keep track of the constant developments in technology and AI systems.

When a person dies in a car crash in the US, data on the incident is typically reported to the National Highway Traffic Safety Administration. Federal law requires that civilian airplane pilots notify the National Transportation Safety Board of in-flight fires and some other incidents. The grim registries are intended to give authorities and manufacturers better insights on ways to improve safety. They helped inspire a crowdsourced repository of artificial intelligence incidents aimed at improving safety in much less regulated areas, such as autonomous vehicles and robotics. The AI Incident Database launched late in 2020 and now contains 100 incidents, including #68, the security robot that flopped into a fountain, and #16, in which Google's photo organizing service tagged Black people as "gorillas."

RPA in law practice has reduced costs by 20-40percent while reducing human error and rising enforcement. Fremont, CA: Law firms are now under and forced to keep pace with the recent digital transformation trends and improvements. As a result, the industry has provided consumers with faster services at a lower cost. It means that law firms and practitioners in this field must change their working methods and become more effective.Traditionally, the legal industry always opposed technical advancements, mainly for cultural purposes. However, in response to consumer demands, the sector is now taking critical measures to catch up.

A new video from human rights organization Amnesty International maps the locations of more than 15,000 cameras used by the New York Police Department, both for routine surveillance and in facial-recognition searches. A 3D model shows the 200-meter range of a camera, part of a sweeping dragnet capturing the unwitting movements of nearly half of the city's residents, putting them at risk for misidentification. The group says it is the first to map the locations of that many cameras in the city. Amnesty International and a team of volunteer researchers mapped cameras that can feed NYPD's much criticized facial-recognition systems in three of the city's five boroughs--Manhattan, Brooklyn, and the Bronx--finding 15,280 in total. Brooklyn is the most surveilled, with over 8,000 cameras.

Shiran is the CEO and Co-Founder at Shield, a Regtech startup out of Israel that is revolutionizing how eComms compliance is handled in FS. For some, artificial intelligence (AI) evokes nightmares of a global takeover by robots. To others, the growing prevalence of AI signifies the end of jobs as humans are benched. Neither reality could be further from the truth. Robots learn, but they can't actually think; they can only do.

Peacefulness is a principal dimension of well-being for all humankind and is the way out of inequity and every single form of violence. Thus, its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed the research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use the SHAP methodology to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions overall, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by Social Good researchers, policy-makers, and peace-builders, with data science tools as powerful as machine learning, could contribute to maximize the societal benefits and minimize the risks to peacefulness.

Recent controversies related to topics such as fake news, privacy, and algorithmic bias have prompted increased public scrutiny of digital technologies and soul-searching among many of the people associated with their development. In response, the tech industry, academia, civil society, and governments have rapidly increased their attention to "ethics" in the design and use of digital technologies ("tech ethics"). Yet almost as quickly as ethics discourse has proliferated across the world of digital technologies, the limitations of these approaches have also become apparent: tech ethics is vague and toothless, is subsumed into corporate logics and incentives, and has a myopic focus on individual engineers and technology design rather than on the structures and cultures of technology production. As a result of these limitations, many have grown skeptical of tech ethics and its proponents, charging them with "ethics-washing": promoting ethics research and discourse to defuse criticism and government regulation without committing to ethical behavior. By looking at how ethics has been taken up in both science and business in superficial and depoliticizing ways, I recast tech ethics as a terrain of contestation where the central fault line is not whether it is desirable to be ethical, but what "ethics" entails and who gets to define it. This framing highlights the significant limits of current approaches to tech ethics and the importance of studying the formulation and real-world effects of tech ethics. In order to identify and develop more rigorous strategies for reforming digital technologies and the social relations that they mediate, I describe a sociotechnical approach to tech ethics, one that reflexively applies many of tech ethics' own lessons regarding digital technologies to tech ethics itself.

Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society. Many methods have been proposed to detect fake images, but they are vulnerable to adversarial perturbations -- intentionally designed noises that can lead to the wrong prediction. Existing methods of attacking fake image detectors usually generate adversarial perturbations to perturb almost the entire image. This is redundant and increases the perceptibility of perturbations. In this paper, we propose a novel method to disrupt the fake image detection by determining key pixels to a fake image detector and attacking only the key pixels, which results in the $L_0$ and the $L_2$ norms of adversarial perturbations much less than those of existing works. Experiments on two public datasets with three fake image detectors indicate that our proposed method achieves state-of-the-art performance in both white-box and black-box attacks.

Design-by-Analogy (DbA) is a design methodology, wherein new solutions are generated in a target domain based on inspiration drawn from a source domain through cross-domain analogical reasoning [1, 2, 3]. DbA is an active research area in engineering design and various methods and tools have been proposed to support the implement of its process [4, 5, 6, 7, 8]. Studies have shown that DbA can help designers mitigate design fixation [9] and improve design ideation outcomes [10]. Fig.1 presents an example of DbA applications [11]. This case aims to solve an engineering design problem: How might we rectify the loud sonic boom generated when trains travel at high speeds through tunnels in atmospheric conditions [11, 12]? For potential design solutions to this problem, engineers explored structures in other design fields than trains or in the nature that effectively "break" the sonic-boom effect. When looking into the nature, engineers discovered that kingfisher birds could slice through the air and dive into the water at extremely high speeds to catch prey while barely making a splash. By analogy, engineers re-designed the train's front-end nose to mimic the geometry of the kingfisher's beak. This analogical design reduced noise and eliminated tunnel booms.