With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers processing unwieldy document collections. One such setting is the Civil Rights Litigation Clearinghouse (CRLC) (https://clearinghouse.net),which posts information about large-scale civil rights lawsuits, serving lawyers, scholars, and the general public. Today, summarization in the CRLC requires extensive training of lawyers and law students who spend hours per case understanding multiple relevant documents in order to produce high-quality summaries of key events and outcomes. Motivated by this ongoing real-world summarization effort, we introduce Multi-LexSum, a collection of 9,280 expert-authored summaries drawn from ongoing CRLC writing. Multi-LexSum presents a challenging multi-document summarization task given the length of the source documents, often exceeding two hundred pages per case. Furthermore, Multi-LexSum is distinct from other datasets in its multiple target summaries, each at a different granularity (ranging from one-sentence "extreme" summaries to multi-paragraph narrations of over five hundred words). We present extensive analysis demonstrating that despite the high-quality summaries in the training data (adhering to strict content and style guidelines), state-of-the-art summarization models perform poorly on this task. We release Multi-LexSum for further research in summarization methods as well as to facilitate development of applications to assist in the CRLC's mission at https://multilexsum.github.io.

The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA.

Decision-making systems based on AI and machine learning have been used throughout a wide range of real-world scenarios, including healthcare, law enforcement, education, and finance. It is no longer far-fetched to envision a future where autonomous systems will be driving entire business decisions and, more broadly, supporting large-scale decision-making infrastructure to solve society's most challenging problems. Issues of unfairness and discrimination are pervasive when decisions are being made by humans, and remain (or are potentially amplified) when decisions are made using machines with little transparency, accountability, and fairness. In this paper, we introduce a framework for \textit{causal fairness analysis} with the intent of filling in this gap, i.e., understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms that generate the disparity in the first place, challenge we call the Fundamental Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, we study the problem of decomposing variations and empirical measures of fairness that attribute such variations to structural mechanisms and different units of the population. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature. Finally, we study which causal assumptions are minimally needed for performing causal fairness analysis and propose a Fairness Cookbook, which allows data scientists to assess the existence of disparate impact and disparate treatment.

The Provisions appear to be an elaboration on the 2019 "Regulations on the Administration of Online Audio and Video Information Services," which broadly banned the use of machine-generated images, audio and video to create or spread "rumors"2,3. The new regulations are aimed at deep synthesis service providers and emphasize cybersecurity, real-name verification of users, data management, marking of synthetic content to alert viewers and "dispelling rumors"1. They expand the Chinese government's efforts to prevent social and political disruption by increasing its control of the Internet. These efforts are tied to the actions of tech platforms and companies. Article 5 encourages industry organizations to establish industry standards and self-discipline systems while "accept[ing] societal oversight".

Fox News contributor and New Orleans resident Raymond Arroyo discusses the ongoing crime crisis as New Orleans' murder rate climbs to one of the highest in the world on'Fox & Friends Weekend.' New Orleans city leaders approved a measure Thursday to reinstate the use of facial recognition software as an investigative tool, despite the technology raising privacy concerns, as crime continues to plague the city. The City Council passed a resolution in a 4-2 vote to use the controversial software technology, which is used in tandem with the Real Time Crime Center, a network of more than 500 cameras across the city, WDSU-TV reported. Speaking in support of the technology, police officials pointed to a policy of how facial recognition can be used with measures ensuring accuracy and rules to make sure it is not used as probable cause, NOLA.com reported. In a statement, the New Orleans Police Department thanked the council for passing the ordinance. Police vehicles block access to Bourbon Street in New Orleans, Louisiana.

In the last ten years, advances in the field of Artificial Intelligence have been impressive with many achievements such as the defeat of the best Go players against AlphaGo, the AI-based computer program. To solve these difficult problems, the resolution algorithms are becoming more and more sophisticated and complex: therefore, the interpretability of Deep Learning models is difficult. Moreover, that complexity can be an obstacle to the use of deep learning algorithms (business and operational users will not understand the algorithm and, at the end, will not adhere to the methodology) and easily lead to biases and even ethical problems (e.g. The notion of interpretability is thus important: by using specific models or interpretability methods, it is possible to make the results but also the problem much more understandable and easily explainable for human beings. Potential biases are more easily detectable and avoidable.

When Microsoft unleashed Tay, its AI-powered chatbot, on Twitter on 23 March 2016, the software giant's hope was that it would "engage and entertain people… through casual and playful conversation". An acronym for'thinking about you', Tay was designed to mimic the language patterns of a 19-year-old American girl and learn by interacting with human users on the social network. Within hours, things had gone badly wrong. Trolls tweeted politically incorrect phrases at the bot in a bid to manipulate its behaviour. Sure enough, Tay started spewing out racist, sexist and other inflammatory messages to its following of more than 100,000 users. Microsoft was forced to lock the @TayandYou account indefinitely less than a day later, but not before its creation had tweeted more than 96,000 times.

Artificial intelligence has been described as a'general purpose technology'. This means that, like electricity, computers and the internet before it, AI is expected to have applications in every corner of society. Unfortunately for organisations seeking to keep their IT secure, this includes cybercrime. In 2020, a study by European police agency Europol and security provider Trend Micro, identified how cybercriminals are already using AI to make their attacks more effective, and the many ways AI will power cybercrime in future. "Cybercriminals have always been early adopters of the latest technology and AI is no different," said Martin Roesler, head of forward-looking threat research at Trend Micro, when the report was published. "It is already being used for password guessing, CAPTCHA-breaking and voice cloning, and there are many more malicious innovations in the works."

Abstract. Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the

The UK government launched a trio of documents concerning AI on 18 July, all with the general purpose of fostering innovation, increasing public trust in the technology and giving clarity to business. But for the detail that will allow this, there is a wait until at least the end of the year when the government will publish its white paper on AI regulation, itself another pause for reflection. The department responsible is clear on the UK approach differing from that of the EU as regulation will be spread across six bodies rather than one dedicated regulator in the EU, but less clear on what the regulation will be for now other than so light-touch that it may just be guidance. In the meantime, one of the documents calls for views on regulation over the next 10 weeks. Last September, a trio of agencies (the Department for Digital, Culture, Media and Sport, the Department for Business, Engergy and Industrial Strategy and the Office for Artificial Intelligence) released the National AI Strategy guidance which promised useful developments such as a transparency standard for AI coding, something of a world-first (subsequently published in December).