Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance.

The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences. However, this expansion has also provoked ethical concerns, such as privacy breaches, algorithmic discrimination, security and reliability issues, transparency, and other unintended consequences. To determine whether a global consensus exists regarding the ethical principles that should govern AI applications and to contribute to the formation of future regulations, this paper conducts a meta-analysis of 200 governance policies and ethical guidelines for AI usage published by public bodies, academic institutions, private companies, and civil society organizations worldwide. We identified at least 17 resonating principles prevalent in the policies and guidelines of our dataset, released as an open source database and tool. We present the limitations of performing a global-scale analysis study paired with a critical analysis of our findings, presenting areas of consensus that should be incorporated into future regulatory efforts.

Meet the authors: Nicholas Kluge Corrêa and Camila Trindade Galvão.

Our paper, "Worldwide AI Ethics", constitutes a meta-analysis of AI guidelines conducted by the AI Ethics Robotics Society, a non-profit organization dedicated to promoting awareness and conducting research on ethical inquiries concerning intelligent and autonomous systems. This research stems from our curiosity about comprehending the global landscape surrounding the normative discourse centered on AI.

Viés da Escalada, Daemons de Otimização, e a Influência da Narrativa Social Aceleracionista / Hill-Climbing Bias, Optimization Daemons, and the Influence of Accelerated Social Narratives

O fenômeno de aceleração social, intimamente ligado a nossa modernização tecnológica e os sistemas políticos e sociais que adotamos, vem sendo alvo de questionamentos por parte da teoria crítica por diversos filósofos e sociólogos, principalmente em relação a se tal "aceleração" seja algo que, possa ser justificável pelo bem comum da sociedade. De fato, as rápidas mudanças que ocorreram no último século causaram uma tremenda mudança em nossos estilos-de-vida, e na maneira como experienciamos o mundo. Que a nossa sociedade mudou e continua a mudar é um fato evidente quando olhamos criticamente para o passado e presente, e comparamos diferentes épocas da história humana. Neste ensaio tentaremos explorar algumas possíveis hipóteses que fundamentem o comportamento aceleracionista em certos fatores e mecanismo biológicos que caracterizam os sistemas de motivação e saciação humanos. Também tentaremos mostrar como certos fenômenos sociais podem auxiliar em fortalecer este tipo de comportamento, e suas possíveis origens evolutivas. Este estudo tem como objetivo principal fundamentar a Tese Aceleracionista em evidências neurofisiológicas, cognitivo-comportamentais, evolutivas e sociais.

The phenomenon of social acceleration is closely linked to our technological modernization and the political and social systems we have adopted, and it has been questioned by several philosophers and sociologists, especially in relation to whether such acceleration is something that can be justified for the common good of society. In fact, the rapid changes that have occurred in the last century have caused a tremendous change in our lifestyles, and in the way we experience the world. That society have changed and continues to change is an evident fact when we look critically to the past and our present and compare different times in human history. In this essay we will try to explore some possible hypotheses that underpin accelerated behavior, in certain biological factors and mechanisms that characterize human motivation and satiation systems. We will also try to show how certain social phenomena can help to strengthen this type of behavior, and its possible evolutionary origins. The main objective of this study is to base the Accelerationist Thesis on neurophysiological, cognitive-behavioral, evolutionary and also social evidence.

Development of a skateboarding trick classifier using accelerometry and machine learning

Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU) use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN). Methods State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE). The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation). Results The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.