Generator: the generator generates new data instances that are "similar" to the training data, in our case celebA images. Generator takes random latent vector and outputs a "fake" image of the same size as our reshaped celebA image. Discriminator: the discriminator evaluate the authenticity of provided images; it classifies the images from the generator and the original image. Discriminator takes true of fake images and outputs the probability estimate ranging between 0 and 1. Here, D refers to the discriminator network, while G obviously refers to the generator.

The United Nations High Commissioner for Human Rights Michelle Bachelet on Wednesday called for a moratorium on the sale and use of artificial intelligence (AI) systems that threaten human rights until adequate safeguards are in place to ensure the technology will not be abused. "We cannot afford to continue playing catch-up regarding AI – allowing its use with limited or no boundaries or oversight, and dealing with the almost inevitable human rights consequences after the fact," Bachelet said in a press release. The UN human rights office released a report on Wednesday warning of the risks of AI technologies, and emphasising that while AI can serve as a force for good, it can also cause catastrophic effects if used irresponsibly. "The complexity of the data environment, algorithms and models underlying the development and operation of AI systems, as well as intentional secrecy of government and private actors are factors undermining meaningful ways for the public to understand the effects of AI systems on human rights and society," the report states. Bachelet, who is the UN's human rights chief, stressed that AI applications that do not comply with international human rights law must be banned.

The European Commission's AI regulation proposal is a proposal for a regulation of the European parliament and of the Council laying down harmonized rules on artificial intelligence, Artificial Intelligence Act and amending certain union legislative acts (published in April 2021). Its explanatory memorandum explicitly aims to implement, among others, an ecosystem of trust by proposing a legal framework for trustworthy AI and the word trust is mentioned several times (14 trust, 1 trusted, 2 trustful, 21 trustworthy, 3 trustworthiness, 6 entrusted, 1 entrusting). This is somewhat surprising from a Swiss legal point of view. Indeed, under Swiss law, trust (German: Vertrauen / Italian: Fiducia / French: Confiance) is never mentioned, for example, in the Swiss Civil Code, in the Code of Obligations, nor in the Federal Product Liability Act, which constitute fundamental legal bases. However, we start seeing this trend also in Switzerland: The second key objective of the Digital Switzerland Strategy is guaranteeing security, trust and transparency.

The United Nations has urged a moratorium on artificial intelligence systems, such as face scanning and social credit systems, that could be a threat to human rights. Michelle Bachelet, the high commissioner for human rights, said countries should ban AI applications that do not comply with international law. Applications that should be prohibited include government "social scoring" systems that judge people based on their behaviour and certain AI-based tools that categorize people into clusters such as by ethnicity or gender. AI-based technologies can be a force for good but they can also "have negative, even catastrophic, effects if they are used without sufficient regard to how they affect people's human rights," Bachelet said in a statement. Her comments came with a new UN report that examines how countries and businesses have rushed into applying AI systems that affect people's lives and livelihoods without setting up proper safeguards to prevent discrimination and other harms.

Artificial intelligence (AI) and automation are continually changing the way we do business. Organisations across all industries and sectors are deploying machine learning and NLP (natural language processing) technologies to automate processes in almost every part of their operation. For businesses, AI means improving efficiencies, amplifying productivity and reducing cost. But while there are many advantages, AI also presents a wide range of legal challenges – especially in areas such as regulatory compliance, liability, risk, privacy and ethics. To compound matters, regulation of AI is slow to develop, leaving businesses with no choice but to navigate the unknown.

Queensland police are preparing to begin trials of an artificial intelligence system to identify high-risk domestic violence offenders, and officers intend to use the data to "knock on doors" before serious escalation. The "actuarial tool" uses data from the police Qprime computer system to develop a risk assessment of all potential domestic and family violence offenders. The algorithm has been in development for about three years and practical trials will begin in some police districts before the end of 2021. "With these perpetrators, we will not wait for a triple-zero phone call and for a domestic and family violence incident to reach the point of crisis," acting Supt Ben Martain said. "Rather, with this cohort of perpetrators, who our predictive analytical tools tell us are most likely to escalate into further DFV offending, we are proactively knocking on doors without any call for service."

Urgent action is needed as it can take time to assess and address the serious risks this technology poses to human rights, warned the High Commissioner: "The higher the risk for human rights, the stricter the legal requirements for the use of AI technology should be". Ms. Bachelet also called for AI applications that cannot be used in compliance with international human rights law, to be banned. "Artificial intelligence can be a force for good, helping societies overcome some of the great challenges of our times. But AI technologies can have negative, even catastrophic, effects if they are used without sufficient regard to how they affect people's human rights". On Tuesday, the UN rights chief expressed concern about the "unprecedented level of surveillance across the globe by state and private actors", which she insisted was "incompatible" with human rights.

The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this paper, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, the sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. The objective of the sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the sheet acts as a useful introductory document on emotion recognition (complementing survey articles).

Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse. In this paper, we propose a method Contrastive Attributed explanations for Text (CAT) which provides contrastive explanations for natural language text data with a novel twist as we build and exploit attribute classifiers leading to more semantically meaningful explanations. To ensure that our contrastive generated text has the fewest possible edits with respect to the original text, while also being fluent and close to a human generated contrastive, we resort to a minimal perturbation approach regularized using a BERT language model and attribute classifiers trained on available attributes. We show through qualitative examples and a user study that our method not only conveys more insight because of these attributes, but also leads to better quality (contrastive) text. Moreover, quantitatively we show that our method is more efficient than other state-of-the-art methods with it also scoring higher on benchmark metrics such as flip rate, (normalized) Levenstein distance, fluency and content preservation.

The U.N. human rights chief is calling for a moratorium on the use of artificial intelligence technology that poses a serious risk to human rights, including face-scanning systems that track people in public spaces. Michelle Bachelet, the U.N. High Commissioner for Human Rights, also said Wednesday that countries should expressly ban AI applications which don't comply with international human rights law. Applications that should be prohibited include government "social scoring" systems that judge people based on their behavior and certain AI-based tools that categorize people into clusters such as by ethnicity or gender. AI-based technologies can be a force for good but they can also "have negative, even catastrophic, effects if they are used without sufficient regard to how they affect people's human rights," Bachelet said in a statement. Her comments came along with a new U.N. report that examines how countries and businesses have rushed into applying AI systems that affect people's lives and livelihoods without setting up proper safeguards to prevent discrimination and other harms.