One approach to defining Intention is to use the counterfactual tools developed to define Causality. Direct Intention is considered the highest level of intent in the common law, and is a sufficient component for the most serious crimes to be committed. Loosely defined it is the commission of actions to bring about a desired or targeted outcome. Direct Intention is not always necessary for the most serious category of crimes because society has also found it necessary to develop a theory of intention around side-effects, known as oblique intent or indirect intent. This is to prevent moral harms from going unpunished which were not the aim of the actor, but were natural consequences nevertheless. This paper uses a canonical example of a plane owner, planting a bomb on their own plane in order to collect insurance, to illustrate how two accounts of counterfactual intent do not conclude that murder of the plane's passengers and crew were directly intended. We extend both frameworks to include a definition of oblique intent developed in Ashton (2021)

Erich Schweighofer is Professor of Legal Informatics, International Law and European Law and head of the Centre for Computers and Law at the University of Vienna. He leads the Centre for Computers and Law – one of the top – 10 research groups in Legal Informatics worldwide. Erich Schweighofer is an international expert in legal informatics and internet governance. He has been involved in many research projects, above all, text analysis, data protection, surveillance technologies and IT security. He is also one of the main organiser of IRIS – a Legal Informatics symposium – and is also active at OCG, GI, CEPIS, IAAIL, FALM and ICANN, where he is now a member of the EURALO board and theCCWG Accountability.

Kate Crawford studies the social and political implications of artificial intelligence. She is a research professor of communication and science and technology studies at the University of Southern California and a senior principal researcher at Microsoft Research. Her new book, Atlas of AI, looks at what it takes to make AI and what's at stake as it reshapes our world. You've written a book critical of AI but you work for a company that is among the leaders in its deployment. How do you square that circle?

THANKS ESPECIALLY to ubiquitous camera-phones, today's wars have been filmed more than any in history. Consider the growing archives of Mnemonic, a Berlin charity that preserves video that purports to document war crimes and other violations of human rights. If played nonstop, Mnemonic's collection of video from Syria's decade-long war would run until 2061. Mnemonic also holds seemingly bottomless archives of video from conflicts in Sudan and Yemen. Even greater amounts of potentially relevant additional footage await review online.

Anticipating the rise in Artificial Intelligence’s ability to produce original works of literature, this study suggests that literariness, or that which constitutes a text as literary, is understudied in relation to text generation. From a computational perspective, literature is particularly challenging because it typically employs figurative and ambiguous language. Literary expertise would be beneficial to understanding how meaning and emotion are conveyed in this art form but is often overlooked. We propose placing experts from two dissimilar disciplines – machine learning and literary studies – in conversation to improve the quality of AI writing. Concentrating on evaluation as a vital stage in the text generation process, the study demonstrates that benefit could be derived from literary theoretical perspectives. This knowledge would improve algorithm design and enable a deeper understanding of how AI learns and generates. This article appears in the special track on AI and Society.

Jurisprudence has always had to face new challenges posed by innovations, socio-economic developments, and changes in the political landscape. Most recently, various aspects of our life are increasingly becoming entangled with artificial intelligence (AI). The legal fraternity requires much better acquaintance with the technical space as the new policies that they will debate will directly influence the products developed by engineers. To understand the parallelism which one can draw between Artificial Intelligence and Law, let's walk through a few autonomous systems where AI is already confronting the legal field. Constant advancements across a spectrum of technologies brought autonomous cars to reality straight out of sci-fi movies.

One interesting development in the space sector is the rise of artificial intelligence. AI has long played an important role in space missions, and its role only continues to grow. But as AI becomes more central to humanity's engagement with space, it raises legal and ethical challenges. Space law is well poised to respond to these challenges. To learn more about AI and space law, we spoke to Anne-Sophie Martin.

Franz Joseph Gall gained fame and notoriety in the 1800s for his theories about the mind. Gall believed that the shapes and bumps of the skull provided a lot of information about a person. These theories, phrenology, attributed skull shapes and bumps to personality, traits and morality. In the 1830s and 1840s, phrenology gained popularity in the USA. Based on skull measurements, physician Charles Caldwell claimed Africans were mentally inferior.

Microsoft Corp. on Friday blamed "accidental human error" for its Bing search engine not showing results for the query "tank man" in the United States and elsewhere after users raised concerns about possible censorship around the Tiananmen Square crackdown anniversary. Users, including in Germany and Singapore, reported Friday that when they performed the search Bing returned the message, "There are no results for tank man." Hours after Microsoft acknowledged the issue, the same search returned only pictures of tanks elsewhere in the world. "Tank man" is often used to describe an unidentified person famously pictured standing before tanks in China's Tiananmen Square during pro-democracy demonstrations in June 1989. Microsoft said the issue was "due to an accidental human error and we are actively working to resolve this."

Exploring meaningful structural regularities embedded in networks is a key to understanding and analyzing the structure and function of a network. The node-attribute information can help improve such understanding and analysis. However, most of the existing methods focus on detecting traditional communities, i.e., groupings of nodes with dense internal connections and sparse external ones. In this paper, based on the connectivity behavior of nodes and homogeneity of attributes, we propose a principle model (named GNAN), which can generate both topology information and attribute information. The new model can detect not only community structure, but also a range of other types of structure in networks, such as bipartite structure, core-periphery structure, and their mixture structure, which are collectively referred to as generalized structure. The proposed model that combines topological information and node-attribute information can detect communities more accurately than the model that only uses topology information. The dependency between attributes and communities can be automatically learned by our model and thus we can ignore the attributes that do not contain useful information. The model parameters are inferred by using the expectation-maximization algorithm. And a case study is provided to show the ability of our model in the semantic interpretability of communities. Experiments on both synthetic and real-world networks show that the new model is competitive with other state-of-the-art models.