The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying its validity--especially exclusion restrictions--is largely rhetorical. We propose using large language models (LLMs) to search for new IVs through narratives and counterfactual reasoning, similar to how a human researcher would. The stark difference, however, is that LLMs can dramatically accelerate this process and explore an extremely large search space. We demonstrate how to construct prompts to search for potentially valid IVs. We contend that multi-step and role-playing prompting strategies are effective for simulating the endogenous decision-making processes of economic agents and for navigating language models through the realm of real-world scenarios. We apply our method to three well-known examples in economics: returns to schooling, supply and demand, and peer effects. We then extend our strategy to finding (i) control variables in regression and difference-in-differences and (ii) running variables in regression discontinuity designs.

Ofcom has warned tech firms that content from chatbots impersonating real and fictional people could fall foul of the UK's new digital laws. The communications regulator issued the guidance after it emerged that users on the Character.AI platform had created avatars mimicking the deceased British teenagers Brianna Ghey and Molly Russell. Under pressure from digital safety campaigners to clarify the situation, Ofcom underlined that content created by user-made chatbots would come under the scope of the Online Safety Act. Without naming the US-based artificial intelligence firm Character.AI, Ofcom said a site or app that allowed users to create their own chatbots for other people to interact with would be covered by the act. "This includes services that provide tools for users to create chatbots that mimic the personas of real and fictional people, which can be submitted to a chatbot library for others to interact with," said Ofcom. In an open letter, Ofcom also said any user-to-user site or app – such as a social media platform or messaging app – that enabled people to share content generated by a chatbot on that site with others would also be in scope.

Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place as a result of data buyers and data sellers being in contact with one another, either directly or through mediating agents. It serves as a coordinating mechanism by which several functions, including the pricing and the distribution of data as the most important ones, interact to make the value of data fully exploited and enhanced. In this article, we present a comprehensive survey of this important and emerging direction from the aspects of data search, data productization, data transaction, data pricing, revenue allocation as well as privacy, security, and trust issues. We also investigate the government policies and industry status of data markets across different countries and different domains. Finally, we identify the unresolved challenges and discuss possible future directions for the development of data markets.

With the recent advance of representation learning algorithms on graphs (e.g., DeepWalk/GraphSage) and natural languages (e.g., Word2Vec/BERT) , the state-of-the art models can even achieve human-level performance over many downstream tasks, particularly for the task of node and sentence classification. However, most algorithms focus on large-scale models for static graphs and text corpus without considering the inherent dynamic characteristics or discovering the reasons behind the changes. This dissertation aims to efficiently model the dynamics in graphs (such as social networks and citation graphs) and understand the changes in texts (specifically news titles and personal biographies). To achieve this goal, we utilize the renowned Personalized PageRank algorithm to create effective dynamic network embeddings for evolving graphs. Our proposed approaches significantly improve the running time and accuracy for both detecting network abnormal intruders and discovering entity meaning shifts over large-scale dynamic graphs. For text changes, we analyze the post-publication changes in news titles to understand the intents behind the edits and discuss the potential impact of titles changes from information integrity perspective. Moreover, we investigate self-presented occupational identities in Twitter users' biographies over five years, investigating job prestige and demographics effects in how people disclose jobs, quantifying over-represented jobs and their transitions over time.

Artificial intelligence (AI) and machine learning often address challenges that are relatively monolithic: determine the safest action for an autonomous car; translate a document from English to French; analyse a medical image to detect a cancer; answer a question about a difficult topic. These kinds of challenge are important and worthwhile targets for AI research. However, an alternative set of challenges exist that are collective in nature: help to minimise a pandemic's impact by coordinating mitigating interventions; help to manage an extreme weather event using real-time physical and social data streams; help to avoid a stock market crash by managing interactions between trading agents; help to guide city developers towards more sustainable coordinated city planning decisions; help people with diabetes to collaboratively manage their condition while preserving privacy.

AI has made significant strides recently, leading to various applications in both civilian and military sectors. The military sees AI as a solution for developing more effective and faster technologies. While AI offers benefits like improved operational efficiency and precision targeting, it also raises serious ethical and legal concerns, particularly regarding human rights violations. Autonomous weapons that make decisions without human input can threaten the right to life and violate international humanitarian law. To address these issues, we propose a three-stage framework (Design, In Deployment, and During/After Use) for evaluating human rights concerns in the design, deployment, and use of military AI. Each phase includes multiple components that address various concerns specific to that phase, ranging from bias and regulatory issues to violations of International Humanitarian Law. By this framework, we aim to balance the advantages of AI in military operations with the need to protect human rights.

OpenAI has notched a victory in its ongoing legal fight against publishers over how its AI tools use creative work. OpenAI argued that the publishers had no legal standing to bring this claim, stating they failed to offer proof that ChatGPT was trained on their material, let alone that the training was harmful. Judge Colleen McMahon of the US Southern District of New York agreed with OpenAI's argument, dismissing the case for lack of standing. "We build our AI models using publicly available data, in a manner protected by fair use and related principles, and supported by long-standing and widely accepted legal precedents," says OpenAI spokesperson Jason Deutrom. Although this is a major setback for Alternet and Raw Story, it's not necessarily the end.

OpenAI is facing multiple lawsuits over its use of several publications' and books' content to train its large language models without explicit permission or proper compensation. A judge has just dismissed one of them. New York federal judge Colleen McMahon has dismissed the lawsuit filed by Raw Story and AlterNet, which accused the company of using their materials for AI training without consent. McMahon explained that the plaintiffs failed to show that they suffered "a cognizable injury" from those actions and that the harm they had cited was "not the type of harm that has been elevated" to warrant a lawsuit. The judge also said that "the likelihood that ChatGPT would output plagiarized content from one of [their] articles seems remote."

A man who pretended to be a paramedic has been found guilty of raping and sexually assaulting women he met on an online dating website. Jamie Kadolski, 24, of Ladysmith Road, Norwich, was found guilty of committing nine sexual offences over an 18-month period. During the trial at Norwich Crown Court he denied the charges made by four different women, which he met on Tinder. The court had previously heard how the former ambulance call handler had told the women he was a paramedic and had used stickers to hide his real role on his work ID card.SuppliedKadolski worked in medical sector but never as a paramedic Kadolski worked as a call handler for the East of England Ambulance Service. The prosecution told the jury that he used stickers to hide his more junior role, so he could claim to the women he met that he was a paramedic.

Graph Neural Networks (GNNs) are a powerful technique for machine learning on graph-structured data, yet they pose interpretability challenges, especially for non-expert users. Existing GNN explanation methods often yield technical outputs such as subgraphs and feature importance scores, which are not easily understood. Building on recent insights from social science and other Explainable AI (XAI) methods, we propose GraphXAIN, a natural language narrative that explains individual predictions made by GNNs. We present a model-agnostic and explainer-agnostic XAI approach that complements graph explainers by generating GraphXAINs, using Large Language Models (LLMs) and integrating graph data, individual predictions from GNNs, explanatory subgraphs, and feature importances. We define XAI Narratives and XAI Descriptions, highlighting their distinctions and emphasizing the importance of narrative principles in effective explanations. By incorporating natural language narratives, our approach supports graph practitioners and non-expert users, aligning with social science research on explainability and enhancing user understanding and trust in complex GNN models. We demonstrate GraphXAIN's capabilities on a real-world graph dataset, illustrating how its generated narratives can aid understanding compared to traditional graph explainer outputs or other descriptive explanation methods.