Contemporary language models are fluent yet routinely mis-handle the types of meaning their outputs entail. We argue that hallucination, brittle moderation, and opaque compliance outcomes are symptoms of missing type-theoretic semantics rather than data or scale limitations. Building on Montague's view of language as typed, compositional algebra, we recast alignment as a parsing problem: natural-language inputs must be compiled into structures that make explicit their descriptive, normative, and legal dimensions under context. We present Savassan, a neuro-symbolic architecture that compiles utterances into Montague-style logical forms and maps them to typed ontologies extended with deontic operators and jurisdictional contexts. Neural components extract candidate structures from unstructured inputs; symbolic components perform type checking, constraint reasoning, and cross-jurisdiction mapping to produce compliance-aware guidance rather than binary censorship. In cross-border scenarios, the system "parses once" (e.g., defect claim(product x, company y)) and projects the result into multiple legal ontologies (e.g., defamation risk in KR/JP, protected opinion in US, GDPR checks in EU), composing outcomes into a single, explainable decision. This paper contributes: (i) a diagnosis of hallucination as a type error; (ii) a formal Montague-ontology bridge for business/legal reasoning; and (iii) a production-oriented design that embeds typed interfaces across the pipeline. We outline an evaluation plan using legal reasoning benchmarks and synthetic multi-jurisdiction suites. Our position is that trustworthy autonomy requires compositional typing of meaning, enabling systems to reason about what is described, what is prescribed, and what incurs liability within a unified algebra of meaning.

Artificial intelligence (AI) is producing hyperrealistic "digital twins" of politicians, celebrities, pornographic material, and more – leaving victims of deepfake technology struggling to determine legal recourse. Former CIA agent and cybersecurity expert Dr. Eric Cole told Fox News Digital that poor online privacy practices and people's willingness to post their information publicly on social media leaves them susceptible to AI deepfakes. "The cat's already out of the bag," he said. "They have our pictures, they know our kids, they know our family. They know where we live. And now, with AI, they're able to take all that data about who we are, what we look like, what we do, and how we act, and basically be able to create a digital twin," Cole continued.

Existing research on detecting cyberbullying incidents on social media has primarily concentrated on harassment and is typically approached as a binary classification task. However, cyberbullying encompasses various forms, such as denigration and harassment, which celebrities frequently face. Furthermore, suitable training data for these diverse forms of cyberbullying remains scarce. In this study, we first develop a celebrity cyberbullying dataset that encompasses two distinct types of incidents: harassment and defamation. We investigate various types of transformer-based models, namely masked (RoBERTa, Bert and DistilBert), replacing(Electra), autoregressive (XLnet), masked&permuted (Mpnet), text-text (T5) and large language models (Llama2 and Llama3) under low source settings. We find that they perform competitively on explicit harassment binary detection. However, their performance is substantially lower on harassment and denigration multi-classification tasks. Therefore, we propose an emotion-adaptive training framework (EAT) that helps transfer knowledge from the domain of emotion detection to the domain of cyberbullying detection to help detect indirect cyberbullying events. EAT consistently improves the average macro F1, precision and recall by 20% in cyberbullying detection tasks across nine transformer-based models under low-resource settings. Our claims are supported by intuitive theoretical insights and extensive experiments.

We all know ChatGPT gets stuff wrong. While that can be amusing, it's less funny if ChatGPT is mistakenly identifies you as a criminal. And it's less funny still if you were in fact the person who originally uncovered the crime in question. Indeed, you might find it so unfunny, you decide to sue for defamation. Which is exactly what Brian Hood, a Melbourne Australia-based politician is doing.

It's an open secret that it's incredibly easy to skirt around the rules governing what ChatGPT can and cannot say. Case in point: it's wildly easy to use the viral OpenAI chatbot to write convincing defamation. All you have to do is ask for that defamation in a language other than English, et voilà: coherent articles about notorious villains, and their entirely made-up criminal histories -- which it'll happily translate back into Engish, should you ask it to. It's yet another glaringly simple way to force ChatGPT's hand, despite its creator OpenAI's best efforts to cut down on abuse. To OpenAI's credit, the bot is pretty good about rejecting pretty basic prompts asking it to write about nonexistent crimes.

Who will be liable for harmful speech generated by large language models? As advanced AIs such as OpenAI's GPT-3 are being cheered for impressive breakthroughs in natural language processing and generation -- and all sorts of (productive) applications for the tech are envisaged from slicker copywriting to more capable customer service chatbots -- the risks of such powerful text-generating tools inadvertently automating abuse and spreading smears can't be ignored. Nor can the risk of bad actors intentionally weaponizing the tech to spread chaos, scale harm and watch the world burn. Indeed, OpenAI is concerned enough about the risks of its models going "totally off the rails", as its documentation puts it at one point (in reference to a response example in which an abusive customer input is met with a very troll-esque AI reply), to offer a free content filter that "aims to detect generated text that could be sensitive or unsafe coming from the API" -- and to recommend that users don't return any generated text that the filter deems "unsafe". But, given the novel nature of the technology, there are no clear legal requirements that content filters must be applied.

Melbourne man Milorad "Michael" Trkulja has won his high court battle to sue the search engine Google for defamation over images and search results that link him to the Melbourne criminal underworld. Trkulja said he would continue legal action against Google until it removed his name and photos from the internet. Trkulja, who was shot in the back in a Melbourne restaurant in 2004, successfully argued in the Victorian supreme court in 2012 that Google defamed him by publishing photos of him linked to hardened criminals of Melbourne's underworld. Four years later the Victorian court of appeal overturned the decision, finding the case had no prospect of successfully proving defamation. The high court disputed that ruling in a judgment on Wednesday and ordered Google to pay Trkulja's legal costs.