Judges are important policymakers but are less accountable to the public than legislators. One way judges strengthen the legitimacy of their policy choices given low accountability is by providing written justifications based on shared principles, which are then published as judicial opinions. John Rawls argued that "[The U.S. Supreme Court's] role is not merely defensive but to give due and continuing effect to public reason by serving as its institutional exemplar." Presumably, this legitimizing function is best served when the general population can understand the written justifications. In practice, however, judicial opinions tend to be extremely long and written in complicated technical language that is inaccessible except to trained lawyers.

A supermarket chain has become Britain's first to return to fully-staffed checkouts after axing most of its self-service tills after its boss said: 'We like to talk to people.' Booths - which has 27 stores in the North across Lancashire, Cumbria, Yorkshire and Cheshire - has been finding the machines to be'slow, unreliable and impersonal' and decided that'rather than artificial intelligence, we're going for actual intelligence'. Staff at the upmarket firm, dubbed the'northern Waitrose', added that they wanted to ensure customers were served by people with'high levels of warm, personal care'. The move by Booths, which was founded in 1847, has provoked much debate on the benefits of self-checkouts as retailers continue to battle a shoplifting epidemic. The British Independent Retailers Association said there could be a'reality check with the current level of retail theft and self-service tills becoming an expensive risk'. All but two Booths stores will put staff back on the tills - with the exceptions being in the Lake District at Keswick and Windermere which can become very busy at times. Booths managing director Nigel Murray said staff at the northern chain'like to talk to people' Booths managing director Nigel Murray told BBC Radio Lancashire today: 'Our customers have told us this over time, that the self-scan machines that we've got in our stores they can be slow, they can be unreliable, they're obviously impersonal.

Francesca Mani and her mother Dorota join'The Ingraham Angle' to demand accountability for victims. One New Jersey mother is fighting to change laws regarding Artificial Intelligence (AI), after her daughter's face was used to generate a fake nude image and reportedly circulated among her classmates. Dorota Mani says her 14-year-old daughter Francesca was one of several female students at Westfield High, N.J., whose photo was used by another classmate to create the pornographic images using AI. While the girls and the school were made aware of the incident in October, the images were shared last summer. Mani told Fox News Digital that she filed a police report and has been in contact with Westfield High over the incident.

AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative impacts and talks potential federal legislation to control it. Scarlett Johansson is the latest actor to take a stand on artificial intelligence. The "Black Widow" star has taken legal action, per Variety, against an AI image-generating app called Lisa AI: 90s Yearbook & Avatar for her voice and likeness in an ad posted on X, formerly Twitter. Johansson's attorney told the outlet, "We do not take these things lightly. Per our usual course of action in these circumstances, we will deal with it with all legal remedies that we will have."

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasizing the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.

The proliferation of Artificial Intelligence (AI) has sparked an overwhelming number of AI ethics guidelines, boards and codes of conduct. These outputs primarily analyse competing theories, principles and values for AI development and deployment. However, as a series of recent problematic incidents about AI ethics/ethicists demonstrate, this orientation is insufficient. Before proceeding to evaluate other professions, AI ethicists should critically evaluate their own; yet, such an evaluation should be more explicitly and systematically undertaken in the literature. I argue that these insufficiencies could be mitigated by developing a research agenda for a feminist metaethics of AI. Contrary to traditional metaethics, which reflects on the nature of morality and moral judgements in a non-normative way, feminist metaethics expands its scope to ask not only what ethics is but also what our engagement with it should be like. Applying this perspective to the context of AI, I suggest that a feminist metaethics of AI would examine: (i) the continuity between theory and action in AI ethics; (ii) the real-life effects of AI ethics; (iii) the role and profile of those involved in AI ethics; and (iv) the effects of AI on power relations through methods that pay attention to context, emotions and narrative.

Firm disclosures about future prospects are crucial for corporate valuation and compliance with global regulations, such as the EU's MAR and the US's SEC Rule 10b-5 and RegFD. To comply with disclosure obligations, issuers must identify nonpublic information with potential material impact on security prices as only new, relevant and unexpected information materially affects prices in efficient markets. Financial analysts, assumed to represent public knowledge on firms' earnings prospects, face limitations in offering comprehensive coverage and unbiased estimates. This study develops a neural network to forecast future firm earnings, using four decades of financial data, addressing analysts' coverage gaps and potentially revealing hidden insights. The model avoids selectivity and survivorship biases as it allows for missing data. Furthermore, the model is able to produce both fiscal-year-end and quarterly earnings predictions. Its performance surpasses benchmark models from the academic literature by a wide margin and outperforms analysts' forecasts for fiscal-year-end earnings predictions.

Citation recommendation is the task of finding appropriate citations based on a given piece of text. The proposed datasets for this task consist mainly of several scientific fields, lacking some core ones, such as law. Furthermore, citation recommendation is used within the legal domain to identify supporting arguments, utilizing non-scholarly legal articles. In order to alleviate the limitations of existing studies, we gather the first scholarly legal dataset for the task of citation recommendation. Also, we conduct experiments with state-of-the-art models and compare their performance on this dataset. The study suggests that, while BM25 is a strong benchmark for the legal citation recommendation task, the most effective method involves implementing a two-step process that entails pre-fetching with BM25+, followed by re-ranking with SciNCL, which enhances the performance of the baseline from 0.26 to 0.30 MAP@10. Moreover, fine-tuning leads to considerable performance increases in pre-trained models, which shows the importance of including legal articles in the training data of these models.

As large language models (LLMs) have increased in their capabilities, so does their potential for dual use. To reduce harmful outputs, produces and vendors of LLMs have used reinforcement learning with human feedback (RLHF). In tandem, LLM vendors have been increasingly enabling fine-tuning of their most powerful models. However, concurrent work has shown that fine-tuning can remove RLHF protections. We may expect that the most powerful models currently available (GPT-4) are less susceptible to fine-tuning attacks. In this work, we show the contrary: fine-tuning allows attackers to remove RLHF protections with as few as 340 examples and a 95% success rate. These training examples can be automatically generated with weaker models. We further show that removing RLHF protections does not decrease usefulness on non-censored outputs, providing evidence that our fine-tuning strategy does not decrease usefulness despite using weaker models to generate training data. Our results show the need for further research on protections on LLMs.

With the exponential growth in large language models (LLMs), leveraging their emergent properties for specialized domains like finance merits exploration. However, regulated fields such as finance pose unique constraints, requiring domain-optimized frameworks. We present ConFIRM, an LLM-based conversational financial information retrieval model tailored for query intent classification and knowledge base labeling. ConFIRM comprises two modules: 1) a method to synthesize finance domain-specific question-answer pairs, and 2) evaluation of parameter efficient fine-tuning approaches for the query classification task. We generate a dataset of over 4000 samples, assessing accuracy on a separate test set. ConFIRM achieved over 90% accuracy, essential for regulatory compliance. ConFIRM provides a data-efficient solution to extract precise query intent for financial dialog systems.