New regulations proposed for artificial intelligence will affect how businesses develop, use and commercialize AI tools.

BRUSSELS, June 23 (Thomson Reuters Foundation) – From Swedish retailer H&M being fined 35 million euros ($42 million) for recording employees' private data to Britain's Barclays bank accused of spying on its staff, workplace surveillance has come into the spotlight in recent months. On Wednesday, the European Trade Union Institute (ETUI), the European Trade Union Confederation's research arm, said planned regulation by the European Union (EU) to improve privacy does not do enough to stop companies from snooping on their workers in the name of security and efficiency. As artificial intelligence (AI) technology becomes ever more accessible and sophisticated, here's why unions are worried: What kind of surveillance are we talking about? Employee monitoring today can involve software programmes for live monitoring, streaming and recording more than a dozen employees' computer screens at a time. Keystrokes, chat programmes, instant messaging and Skype dialogues may also be monitored and recorded in real time.

Back in 2014, Amazon turned to artificial intelligence (AI) to streamline its recruitment process, using a machine-learning algorithm to review résumés and automate its search for talent. Three years later it abandoned the programme after it became apparent it was biased against female candidates. Because it relied on historical hiring patterns -- mostly of men -- it built in a preference for male hires. Last year, researchers in the US claimed they could predict criminality by running profile pictures through an AI algorithm. The project was roundly condemned, with scientists pointing out that it simply replicated existing racial biases in the criminal justice system, according to a report by tech magazine Wired...

Organizations around the globe are becoming more aware of the risks artificial intelligence (AI) may pose, including bias and potential job loss due to automation. At the same time, AI is providing many tangible benefits for organizations and society. For organization, this is creating a fine line between the potential harm AI might cause and the costs of not adopting the technology. Three emerging practices can help organizations navigate the complex world of moral dilemmas created by autonomous and intelligent systems. AI risks continue to grow, but so does the number of public and private organizations that are releasing ethical principles to guide the development and use of AI.

Digitisation, automation and datafication permeate policing and justice more and more each year -- from predictive policing methods through recidivism prediction to automated biometric identification at the border. The sociotechnical issues surrounding the use of such systems raise questions and reveal problems, both old and new. Our article reviews contemporary issues surrounding automation in policing and the legal system, finds common issues and themes in various different examples, introduces the distinction between human "retail bias" and algorithmic "wholesale bias", and argues for shifting the viewpoint on the debate to focus on both workers' rights and organisational responsibility as well as fundamental rights and the right to an effective remedy.

Ambiguity is a characteristic of natural language, which makes expression ideas flexible. However, in a domain that requires accurate statements, it becomes a barrier. Specifically, a single word can have many meanings and multiple words can have the same meaning. When translating a text into a foreign language, the translator needs to determine the exact meaning of each element in the original sentence to produce the correct translation sentence. From that observation, in this paper, we propose ParaLaw Nets, a pretrained model family using sentence-level cross-lingual information to reduce ambiguity and increase the performance in legal text processing. This approach achieved the best result in the Question Answering task of COLIEE-2021.

SAN ANTONIO, June 22, 2021 (GLOBE NEWSWIRE) -- CloudCommerce, Inc. (CLWD), a technology driven provider of digital advertising solutions, today announced that SWARM, the Company's AI-driven advertising solution, reduced media costs by more than 60% for Energy in Focus, a web based platform that showcases diverse information on energy in California. Based on the first-round results, the client has committed to a second round. Energy in Focus turned to CloudCommerce to better understand which creative initiatives would be best for their different audiences, such as b2b partners and its public advocacy audience. SWARM analyzed the top 5 previous posts from Facebook and used artificial intelligence to develop creative variations which ran on other media platforms. The result: the cost was reduced by more than 60%.

The Linux Foundation has announced a new permissive license designed to help foster collaboration around open data for artificial intelligence (AI) and machine learning (ML) projects. It has often been said that data is the new oil, but for AI and ML projects in particular, having access to expansive and diverse data sets is key to reducing bias and building powerful models capable of all manner of intelligent tasks. To machines, data is a little like "experience" is to humans -- the more of it you have, the better decisions you are likely to make. With CDLA-Permissive-2.0, the Linux Foundation is building on its previous efforts to encourage data-sharing efforts through licensing arrangements that clearly define how the data -- and any derivative data sets -- can and can't be used. The Linux Foundation first introduced the Community Data License Agreement (CDLA) back in 2017 to entice organizations to open up their vast pools of (underused) data to third-parties.

The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.

This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.