Fox News correspondent David Spunt has the latest as the Supreme Court weighs whether tech companies should be legally liable for harmful content on their platforms on'Special Report.' An impending Supreme Court ruling focusing on whether legal protections given to Big Tech extend to their algorithms and recommendation features could have significant implications for future cases surrounding artificial intelligence, according to experts. In late February, the Supreme Court heard oral arguments examining the extent of legal immunity given to tech companies that allow third-party users to publish content on their platforms. One of two cases, Gonzalez v. Google, focuses on recommendations and algorithms used by sites like YouTube, allowing accounts to arrange and promote content to users. Section 230, which allows online platforms significant leeway regarding responsibility for users' speech, has been challenged multiple times in the Supreme Court.

The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason; negative human actions, which include hunting, overfishing, pollution, and the conversion of land for urbanisation and agricultural purposes. Despite significant investment from charities and governments for activities that benefit nature, global wildlife populations continue to decline. Local wildlife guardians have historically played a critical role in global conservation efforts and have shown their ability to achieve sustainability at various levels. In 2021, COP26 recognised their contributions and pledged US$1.7 billion per year; however, this is a fraction of the global biodiversity budget available (between US$124 billion and US$143 billion annually) given they protect 80% of the planets biodiversity. This paper proposes a radical new solution based on "Interspecies Money," where animals own their own money. Creating a digital twin for each species allows animals to dispense funds to their guardians for the services they provide. For example, a rhinoceros may release a payment to its guardian each time it is detected in a camera trap as long as it remains alive and well. To test the efficacy of this approach 27 camera traps were deployed over a 400km2 area in Welgevonden Game Reserve in Limpopo Province in South Africa. The motion-triggered camera traps were operational for ten months and, using deep learning, we managed to capture images of 12 distinct animal species. For each species, a makeshift bank account was set up and credited with {\pounds}100. Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount - mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian.

This is an audit framework for AI-nudging. Unlike the static form of nudging usually discussed in the literature, we focus here on a type of nudging that uses large amounts of data to provide personalized, dynamic feedback and interfaces. We call this AI-nudging (Lanzing, 2019, p. 549; Yeung, 2017). The ultimate goal of the audit outlined here is to ensure that an AI system that uses nudges will maintain a level of moral inertia and neutrality by complying with the recommendations, requirements, or suggestions of the audit (in other words, the criteria of the audit). In the case of unintended negative consequences, the audit suggests risk mitigation mechanisms that can be put in place. In the case of unintended positive consequences, it suggests some reinforcement mechanisms. Sponsored by the IBM-Notre Dame Tech Ethics Lab

Appropriately regulating artificial intelligence is an increasingly urgent policy challenge. Legislatures and regulators lack the specialized knowledge required to best translate public demands into legal requirements. Overreliance on industry self-regulation fails to hold producers and users of AI systems accountable to democratic demands. Regulatory markets, in which governments require the targets of regulation to purchase regulatory services from a private regulator, are proposed. This approach to AI regulation could overcome the limitations of both command-and-control regulation and self-regulation. Regulatory market could enable governments to establish policy priorities for the regulation of AI, whilst relying on market forces and industry R&D efforts to pioneer the methods of regulation that best achieve policymakers' stated objectives.

Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly. We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.

For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types beforehand. Most existing studies in the field of document image classification either focus on single-page documents or treat multiple pages in a document independently. Although in recent years a few techniques have been proposed to exploit the context information from neighboring pages to enhance document page classification, they typically cannot be utilized with large pre-trained language models due to the constraint on input length. In this paper, we present a simple but effective approach that overcomes the above limitation. Specifically, we enhance the input with extra tokens carrying sequential information about previous pages - introducing recurrence - which enables the usage of pre-trained Transformer models like BERT for context-aware page classification. Our experiments conducted on two legal datasets in English and Portuguese respectively show that the proposed approach can significantly improve the performance of document page classification compared to the non-recurrent setup as well as the other context-aware baselines.

Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and attract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science.

"I'll split 50% royalties on any successful AI generated song that uses my voice. "Feel free to use my voice without penalty. I have no label and no legal bindings." The musician's declaration comes in the wake of streaming platforms removing an AI-generated song using simulated voices of Drake and The Weeknd. Universal Music Group (UMG), which represents both artists, called for the purge after "Heart on My Sleeve" garnered over 15 million listens on TikTok and 600,000 on Spotify.

Companies clamor to use Remie Michelle Clarke's voice. An award-winning vocal artist, her smooth, Irish accent backs ads for Mazda and Mastercard and is the sound of Microsoft's search engine, Bing, in Ireland. But in January, her sound engineer told Michelle Clarke he'd found a voice that sounded uncannily like hers someplace unexpected: on Revoicer.com, For a modest monthly fee, Revoicer customers can access hundreds of different voices and, through an artificial intelligence-backed tool, morph them to say anything -- to voice commercials, recite corporate trainings or narrate books. Revoicer advertised "Olivia" with a photo of a gray-haired woman, who appeared to be of Asian descent, and a blurb: "A deep, calm and kind voice. A 38-year-old brunette, Michelle Clarke looked nothing like "Olivia." But when she hit play, she was greeted with the jarring sound of what could only be her own voice: "Hello my dear ones, my name is Olivia," it said. "I have a soft and caring voice."

WASHINGTON – When the U.S. Supreme Court decides in the coming months whether to weaken a powerful shield protecting internet companies, the ruling also could have implications for rapidly developing technologies like artificial intelligence chatbot ChatGPT. The justices are due to rule by the end of June whether Alphabet's YouTube can be sued over its video recommendations to users. That case tests whether a U.S. law that protects technology platforms from legal responsibility for content posted online by their users also applies when companies use algorithms to target users with recommendations. What the court decides about those issues is relevant beyond social media platforms. Its ruling could influence the emerging debate over whether companies that develop generative AI chatbots like ChatGPT from OpenAI, a company in which Microsoft Corp is a major investor, or Bard from Alphabet's Google should be protected from legal claims like defamation or privacy violations, according to technology and legal experts. This could be due to a conflict with your ad-blocking or security software.