As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.

Dec 20 (Thomson Reuters Foundation) -Two days after it was sued by Rohingya refugees from Myanmar over allegations that it did not take action against hate speech, social media company Meta, formerly known as Facebook, announced a new artificial intelligence system to tackle harmful content. Machine learning tools have increasingly become the go-to solution for tech firms to police their platforms, but questions have been raised about their accuracy and their potential threat to freedom of speech. WHY ARE SOCIAL MEDIA FIRMS UNDER FIRE OVER CONTENT MODERATION? The $150 billion Rohingya class-action lawsuit filed this month came at the end of a tumultuous period for social media giants, which have been criticised for failing to effectively tackle hate speech online and increasing polarization. The complaint argues that calls for violence shared on Facebook contributed to real-world violence against the Rohingya community, which suffered a military crackdown in 2017 that refugees said included mass killings and rape.

For centuries, cities in the United States used an inexpensive, malleable, and leak-resistant material for constructing their water pipes: lead. Today, the health risks posed by lead pipes are well-known. Drinking lead-contaminated water can stunt children's development and cause heart and kidney problems among adults.¹ The Environmental Protection Agency (EPA) banned the use of lead pipes for new construction in 1986. Yet, today, lead services lines (the pipes that take water from city lines into individual homes) are still prevalent across the country.

GENEVA – Japan, the United States and other countries have blocked any advancement in U.N. talks toward legally binding measures to ban and regulate the development and use of lethal autonomous weapon systems. The Sixth Review Conference of the Convention on Certain Conventional Weapons ended Friday in Geneva without progress, failing to reflect eight years of work and leaving countries and nongovernmental organizations that have called for legally binding rules expressing disappointment. Also referred to as "killer robots," autonomous weapons are artificial intelligence-powered weapons using facial recognition and algorithms. Once activated, the weapons can select and attack targets without the assistance of a human operator. They pose ethical, legal and security risks.

Large-scale autoregressive language models such as GPT-3 are few-shot learners that can perform a wide range of language tasks without fine-tuning. While these models are known to be able to jointly represent many different languages, their training data is dominated by English, potentially limiting their cross-lingual generalization. In this work, we train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters sets new state of the art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in 4-shot settings) and natural language inference (+5.4% in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, our model outperforms GPT-3 on 171 out of 182 translation directions with 32 training examples, while surpassing the official supervised baseline in 45 directions. We present a detailed analysis of where the model succeeds and fails, showing in particular that it enables cross-lingual in-context learning on some tasks, while there is still room for improvement on surface form robustness and adaptation to tasks that do not have a natural cloze form. Finally, we evaluate our models in social value tasks such as hate speech detection in five languages and find it has limitations similar to comparable sized GPT-3 models.

Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion - we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.

The Council of Europe is taking part in the third edition (online) of the "Athens Roundtable on Artificial Intelligence and the Rule of Law" on 6 and 7 December. Organised by the Future Society and ELONTech under the Patronage of the President of the Hellenic Republic, Katerina Sakellaropoulou, the event is co-hosted by UNESCO, the Council of Europe, the European Parliament's Panel on the Future of Science and Technology (STOA), IEEE SA, the Center on Civil Justice at the NYU School of Law and the National Judicial College, among other institutions. The roundtable is designed to facilitate a participatory dialogue among key stakeholders on international AI policy developments and key AI standardisation and benchmarking initiatives in the US, Europe and beyond. It will also address important issues at the intersection of AI, industry, government and law, including civil liability regimes, regulatory compliance, privacy and consumer protection, and judicial capacity building. Council of Europe Secretary General Marija Pejčinović Burić is speaking at the opening.

With AI systems becoming more powerful and pervasive, there is increasing debate about keeping their actions aligned with the broader goals and needs of humanity. This multi-disciplinary and multi-stakeholder debate must resolve many issues, here we examine three of them. The first issue is to clarify what demands stakeholders might usefully make on the designers of AI systems, useful because the technology exists to implement them. We make this technical topic more accessible by using the framing of cognitive architectures. The second issue is to move beyond an analytical framing that treats useful intelligence as being reward maximization only. To support this move, we define several AI cognitive architectures that combine reward maximization with other technical elements designed to improve alignment. The third issue is how stakeholders should calibrate their interactions with modern machine learning researchers. We consider how current fashions in machine learning create a narrative pull that participants in technical and policy discussions should be aware of, so that they can compensate for it. We identify several technically tractable but currently unfashionable options for improving AI alignment.

Finland is the first country having released its AI strategy in Europe already in March 2017. According to a study committed by Accenture and Frontier Economics, Finland ranked second that year, after the US, among the 11 developed countries in which economic growth potential is made possible by AI. According to Finland, this is because of the country's business structure (technologically intensive) and the public sector degree of digitalisation (see Finland, 2017, p. 12). The national strategy has been commissioned by the Government of Juha Sipilä to the Ministry of Economic Affairs and Employment, which in turn has nominated a steering group on AI to work on the national strategy. The AI Working Group has released the first draft of the strategy in 2017, though the work on the optimum public policies to be implemented is actually an on-going process, which has already been updated in 2019.

Datasets fuel AI models like gasoline (or electricity, as the case may be) fuels cars. Whether they're tasked with generating text, recognizing objects, or predicting a company's stock price, AI systems "learn" by sifting through countless examples to discern patterns in the data. For example, a computer vision system can be trained to recognize certain types of apparel, like coats and scarfs, by looking at different images of that clothing. Beyond developing models, datasets are used to test trained AI systems to ensure they remain stable -- and measure overall progress in the field. Models that top the leaderboards on certain open source benchmarks are considered state of the art (SOTA) for that particular task.