In July 2019, Guillermo Federico Ibarrola was heading home on the subway when he was stopped by Buenos Aires police. The authorities told Ibarrola that he was being detained for an armed robbery that had happened three years ago in a city about 400 miles away. He said he had never even been to the city where he was accused of committing the crime. On the sixth day in police custody, he was suddenly released. The police officers offered Ibarrola coffee and dinner, and a bus ticket back home. As it turned out, a "Guillermo Ibarrola" had potentially committed a crime, but it wasn't this Guillermo Ibarrola.

Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can reveal natural partitions at different resolutions without making a priori assumptions about the number of clusters in the corpus. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods, and also evaluate different text vector embeddings, from classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert. This comparative work is showcased through an analysis of a corpus of US news coverage during the presidential election year of 2016.

Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, "Leading scientists agree that global warming is a serious concern," framing a clause which affirms their own stance ("that global warming is serious") as an opinion endorsed ("[scientists] agree") by a reputable source ("leading"). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: "Mistaken scientists claim [...]." Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other's opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author's own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.

As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to analyse and interpret a predictive model, and vet it thoroughly to ensure that the recourses it offers are meaningful and non-discriminatory before it is deployed in the real world. To this end, we propose a novel model agnostic framework called Actionable Recourse Summaries (AReS) to construct global counterfactual explanations which provide an interpretable and accurate summary of recourses for the entire population. We formulate a novel objective which simultaneously optimizes for correctness of the recourses and interpretability of the explanations, while minimizing overall recourse costs across the entire population. More specifically, our objective enables us to learn, with optimality guarantees on recourse correctness, a small number of compact rule sets each of which capture recourses for well defined subpopulations within the data. We also demonstrate theoretically that several of the prior approaches proposed to generate recourses for individuals are special cases of our framework. Experimental evaluation with real world datasets and user studies demonstrate that our framework can provide decision makers with a comprehensive overview of recourses corresponding to any black box model, and consequently help detect undesirable model biases and discrimination.

Our focus on AI and Data at University of Birmingham is two-fold, covering education to bridge sector skills gaps with Degree Apprenticeships and MSc programmes, alongside well established research communities promoting new ways of working and new insights into data and AI. We know the tech world changes rapidly. We are collaborating with industry sectors such as IT and computer science, engineering and professional services by developing innovative courses whilst also promoting the latest insights from research directly to business. Researchers at University of Birmingham and experts from industry are working on various projects for the UKRI AI for Services network initiatives. We are a partner in The Alan Turing Institute, the UK's national institute for data science and artificial intelligence.

Four Uber drivers in the UK and Portugal who claim they were dismissed unfairly by the company's anti-fraud algorithm have challenged their account deactivations in a European court, citing GDPR protections against automated decision making. The App Drivers & Couriers Union, a UK-based worker advocacy group, filed a legal complaint on Monday in a district court in Amsterdam, Netherlands, on behalf of the dismissed drivers. "Uber has been allowed to violate employment law with impunity for years and now we are seeing a glimpse into an Orwellian world of work where workers have no rights and are managed by machine," said Yaseen Aslam, President of the App Drivers & Couriers Union, in a statement. "If Uber is not checked, this practice will become the norm for everyone." Anton Ekker, the attorney representing the four former drivers – three from the UK and one from Portugal – said in a statement that the case represents the first challenge under the GDPR to automated decisions affecting the estimated 3.9m Uber drivers worldwide. Article 22 of the GDPR states individuals "have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her."

On October 22, the report released at the 2020 Pujiang Innovation Forum Results Conference showed that China published 28,700 AI scientific papers in 2019, an increase of 12.4 percent over the previous year, and its activity in top international conferences in the field of artificial intelligence And the influence continues to increase. During the same period, the number of AI patent applications in China exceeded 30,000, an increase of 52.4 percent over the previous year. From a national perspective, Beijing, Jiangsu, Guangdong and other places published the most research results in 2019. We know you don't want to miss any news or research breakthroughs. Subscribe to our popular newsletter Synced Global AI Weekly to get weekly AI updates.

SparkBeyond, an AI-powered problem-solving platform that augments and accelerates the generation of novel insights out of data and knowledge, alongside Baker McKenzie, a leading multinational law firm, announced a market-first collaboration that will apply SparkBeyond's technology to reimagine legal client services in the future. Baker McKenzie will launch its new global innovation arm, Reinvent, and apply SparkBeyond's AI to predict which services clients will require from law firms, explore unforeseen drivers of client demand and learn how to evolve its business to accommodate those needs. The partnership also aims to reimagine traditional law practices and pave new paths for tens of thousands of firms across the globe. "The legal sector is on a new path for disruption and innovation and our partnership with SparkBeyond will turbo-charge the evolution in our business," said Ben Allgrove, Partner at Baker McKenzie. "Understanding the drivers and root causes driving future client demand will allow us unparalleled insights and the ability to shape the future of our business to create additional value across the legal, tax, and compliance functions in the future."

Robots that analyse a defendant's body language to determine signs of guilt will replace judges by the year 2070, according to an artificial intelligence expert. Writer and speaker on AI Terence Mauri believes the machines will be able to detect physical and psychological signs of dishonesty with 99.9 per cent accuracy. He claims they will be polite, speak every known language fluently and will be able to detect signs of lying that couldn't be detected by a human. Robot judges will have cameras that capture and identify irregular speech patterns, unusually high increases in body temperature and hand and eye movements. Terence Mauri (pictured) is an AI expert, author and founder of Hack Future Lab, a global think tank.

For the first time, lawyers can apply legal analytics to cases heard in New York County Supreme Court ("New York County"). Lex Machina, a subsidiary of RELX, the British information corporate formerly known as Reed Elsevier, is announcing today the publication of data on 119,000 cases. The data is based on both dockets (analogous to the abstracts of academic papers) and documents (the full papers). Numerically, this caseload is not a massive expansion to the 4.5m cases already in Lex Machina's database, but Karl Harris, Lex Machina's CEO, argues it is an important milestone because New York County is such a significant jurisdiction. Lawyers are not renowned for an addiction to statistics and maths.