The AI Robotics Ethics Society (AIRES) is a non-profit organization founded in 2018 by Aaron Hui to promote awareness and the importance of ethical implementation and regulation of AI. AIRES is now an organization with chapters at universities such as UCLA (Los Angeles), USC (University of Southern California), Caltech (California Institute of Technology), Stanford University, Cornell University, Brown University, and the Pontifical Catholic University of Rio Grande do Sul (Brazil). AIRES at PUCRS is the first international chapter of AIRES, and as such, we are committed to promoting and enhancing the AIRES Mission. Our mission is to focus on educating the AI leaders of tomorrow in ethical principles to ensure that AI is created ethically and responsibly. As there are still few proposals for how we should implement ethical principles and normative guidelines in the practice of AI system development, the goal of this work is to try to bridge this gap between discourse and praxis. Between abstract principles and technical implementation. In this work, we seek to introduce the reader to the topic of AI Ethics and Safety. At the same time, we present several tools to help developers of intelligent systems develop "good" models. This work is a developing guide published in English and Portuguese. Contributions and suggestions are welcome.

Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform human-like tasks. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for human users would be to understand and appropriately trust the result of AI algorithms and methods. In this paper, to address this challenge, we study and analyze the recent work done in Explainable Artificial Intelligence (XAI) methods and tools. We introduce a novel XAI process, which facilitates producing explainable models while maintaining a high level of learning performance. We present an interactive evidence-based approach to assist human users in comprehending and trusting the results and output created by AI-enabled algorithms. We adopt a typical scenario in the Banking domain for analyzing customer transactions. We develop a digital dashboard to facilitate interacting with the algorithm results and discuss how the proposed XAI method can significantly improve the confidence of data scientists in understanding the result of AI-enabled algorithms.

The range of application of artificial intelligence (AI) is vast, as is the potential for harm. Growing awareness of potential risks from AI systems has spurred action to address those risks, while eroding confidence in AI systems and the organizations that develop them. A 2019 study found over 80 organizations that published and adopted "AI ethics principles'', and more have joined since. But the principles often leave a gap between the "what" and the "how" of trustworthy AI development. Such gaps have enabled questionable or ethically dubious behavior, which casts doubts on the trustworthiness of specific organizations, and the field more broadly. There is thus an urgent need for concrete methods that both enable AI developers to prevent harm and allow them to demonstrate their trustworthiness through verifiable behavior. Below, we explore mechanisms (drawn from arXiv:2004.07213) for creating an ecosystem where AI developers can earn trust - if they are trustworthy. Better assessment of developer trustworthiness could inform user choice, employee actions, investment decisions, legal recourse, and emerging governance regimes.

As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems. We examine the practices and experiences of researchers and engineers from Australia's national scientific research agency (CSIRO), who are involved in designing and developing AI systems for a range of purposes. Semi-structured interviews were used to examine how the practices of the participants relate to and align with a set of high-level AI ethics principles that are proposed by the Australian Government. The principles comprise: Privacy Protection & Security, Reliability & Safety, Transparency & Explainability, Fairness, Contestability, Accountability, Human-centred Values, and Human, Social & Environmental Wellbeing. The insights of the researchers and engineers as well as the challenges that arose for them in the practical application of the principles are examined. Finally, a set of organisational responses are provided to support the implementation of high-level AI ethics principles into practice.

NGOs and activists have called for a ban on the use of autonomous weapons that are no longer strictly controlled by human hands, calling the so-called "killer robots" a "threat to humanity". The move comes as the Sixth Review Conference of the Convention on Conventional Weapons (CCW) takes place in Geneva this week, chaired by ambassador Yann Hwang of France. Member states are expected to decide whether to negotiate a treaty that prohibits the use of weapons that are not decisively controlled by human hands. Human Rights Watch (HRW) called for a new treaty to clarify and strengthen existing laws related to these new technologies, adding that "the emergence of autonomous weapons systems and the prospect of losing meaningful human control over the use of force are grave threats that demand urgent action". "These are weapons systems that would operate without meaningful human control. That is, instead of a human, you would have the weapon system itself that would select the target and decide when to pull the trigger. You would not have humans performing these functions, instead, artificial intelligence would replace the soldier on the battlefield," explained Steve Goose, director of Human Rights Watch's Arms Division.

Over at "SkepticalScience", which is neither skeptical nor scientific, they're hyping a new "Artificial Intelligence" (AI) tool developed by John Cook et al. to identify "denialist claims". The paper laying out this foolishness is in Nature Scientific Reports in an article with the most sciency title of "Computer-assisted classification of contrarian claims about climate change". "Ultimately, our goal is the Holy Grail of fact-checking, which is being able to detect and debunk misinformation in real time," said Cook, who partly developed the framework previously at George Mason University. Because in total contradiction to point 4 immediately above, that experts are not unreliable, one of the finest physicists of my lifetime, Richard Feynman, famously said: Nature Magazine, a premier scientific journal and a huge defender of the anthropogenic climate change hypothesis, has an article on the subject which says: So clearly, Nature Magazine is a secret nest of climate "denialists" whose claims should be censored before anyone can be misled by them … and while that example alone should be enough to totally discredit their artificial stupidity, it's just the first of many. So it's gonna identify articles pointing out that while in most of the media heatwaves are always explained as climate change, cold spells are just plain old weather … For most species, including humans and coral reefs, a change of a degree in average temperature over fifty years means nothing.

In the world of video games, it was a minor year for releases and a major year for reckonings. In July, the California Department of Fair Employment and Housing filed a lawsuit alleging that Activision Blizzard, the American publisher of the Call of Duty series, had fostered a "frat boy" workplace culture that enabled gender-based discrimination and sexual harassment across the company. Then, in November, an investigation by the Wall Street Journal reported that Activision's C.E.O., Bobby Kotick, was not only long aware of these allegations, which include rape, but also withheld them from the company's board of directors. The report claimed that Kotick himself was the subject of complaints, and that he left one former assistant a voice-mail message threatening to have her killed. A brief employee walkout has matured into an indefinite one; the board has vowed to stand by Kotick, who, if fired, stands to receive a senselessly vast severance package of two hundred and fifty million dollars.

The United States marked International Human Rights Day Friday with the announcement of sanctions on dozens of people and entities tied to rights abuses in China, Myanmar, North Korea and Bangladesh, while blacklisting a Chinese artificial intelligence company. The financial and visa sanctions came on the final day of President Joe Biden's virtual Summit for Democracy, where he unveiled policies to bolster democracy against threats around the world and appealed for solidarity among some 100 participants. "On International Human Rights Day, Treasury is using its tools to expose and hold accountable perpetrators of serious human rights abuse," Deputy Secretary of the Treasury Wally Adeyemo said in a statement. "Our actions today, particularly those in partnership with the United Kingdom and Canada, send a message that democracies around the world will act against those who abuse the power of the state to inflict suffering and repression," he added. The sanctions on China slapped a U.S. visa ban on the current and previous chairmen of the Xinjiang Uyghur Autonomous Region of China (XUAR), Erken Tuniyaz and Shohrat Zakir, and came a day after a tribunal in London found that Chinese policies in the region constituted genocide.

Artificial intelligence offers great potential to positively affect virtually all areas of our lives. There is, however, significant potential for abuse and harm resulting from irresponsible use of AI. Perhaps you are a fan of "Black Mirror" or the "Terminator" series of movies, each of which portend a world where machine intelligence is a threat to humanity, in particular once AI becomes "smarter" than humankind. The concept of the "singularity" (the point where AI surpasses human intelligence) has inspired great science fiction, but it has also prompted warnings regarding responsible use of AI. Studies have also shown that AI systems can be adversely influenced by biased input data or express or inherent biases of programmers.

Population-level societal events, such as civil unrest and crime, often have a significant impact on our daily life. Forecasting such events is of great importance for decision-making and resource allocation. Event prediction has traditionally been challenging due to the lack of knowledge regarding the true causes and underlying mechanisms of event occurrence. In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources. The explosive growth of data and the remarkable advancement in software/hardware technologies have led to applications of deep learning techniques in societal event studies. This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions. We focus on two domains of societal events: \textit{civil unrest} and \textit{crime}. We first introduce how event forecasting problems are formulated as a machine learning prediction task. Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems. Finally, we discuss the challenges in societal event forecasting and put forward some promising directions for future research.