This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.

The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the estimation of causal effects. In this paper, we review one of the most prominent methods - "double/debiased machine learning" (DML) - and empirically evaluate it by comparing its performance on simulated data relative to more traditional statistical methods, before applying it to real-world data. Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various nonlinear confounding relationships. This advantage enables a departure from traditional functional form assumptions typically necessary in causal effect estimation. However, we demonstrate that the method continues to critically depend on standard assumptions about causal structure and identification. When estimating the effects of air pollution on housing prices in our application, we find that DML estimates are consistently larger than estimates of less flexible methods. From our overall results, we provide actionable recommendations for specific choices researchers must make when applying DML in practice.

Michael Cohen will not face sanctions after he cited fake legal cases in a court filing generated by artificial intelligence, a federal judge said Wednesday. Cohen, former President Trump's onetime fixer and lawyer, had pleaded guilty to tax and campaign finance violations and is currently under supervised release. He has repeatedly sought to have his sentence reduced, and in his most recent attempt, Cohen provided his attorney with fabricated case citations he later admitted were generated by Google's AI chatbot, formerly known as Bard. U.S. District Judge Jesse Furman said the false citations were "embarrassing and certainly negligent" in a 13-page order that denied Cohen's fourth motion for early termination of supervised release. But the judge found that Cohen, who had said he misunderstood how AI works and did not intend to cite fake cases, had not acted in "bad faith" and that neither he nor his lawyer, David Schwartz, should be sanctioned.

A group of researchers backed by the French government have released what is thought to be the largest AI training dataset composed entirely of text that is in the public domain. "There's no fundamental reason why someone couldn't train an LLM fairly," says Ed Newton-Rex, CEO of Fairly Trained. He founded the nonprofit in January 2024 after quitting his executive role at image generation startup Stability AI because he disagreed with its policy of scraping content without permission. Fairly Trained offers a certification to companies willing to prove that they've trained their AI models on data that they either own, have licensed, or is in the public domain. When the nonprofit launched, some critics pointed out that it hadn't yet identified a large language model that met those requirements.

At an AI research lab on the edges of Abu Dhabi last year, an international team of 25 computer scientists were putting the finishing touches on a deep learning algorithm before sending it to be trained on 4,000 powerful computer chips. The AI system, which cost several million dollars to train, was funded by an arm of the Abu Dhabi government called the Advanced Technology Research Council (ATRC). Despite the government's substantial investment, ATRC director Faisal Al Bannai decided to release the finished model online for free. If it was as good as the team believed, the boost to the United Arab Emirates' reputation would be all the return the government needed on its investment, he reasoned. When the AI, named Falcon after the UAE's national bird, was publicly released last September, it became a sensation. By some measures it was the best open-source large language model (LLM) available in the world at that point, outperforming top offerings from Meta and Google.

After a relaxing weekend away, Guillermo Ibarrola was walking out of a train station in Argentina's capital when police arrested him and accused him of a robbery committed hundreds of miles away in a place he had never visited. "It was a nightmare," Ibarrola told local media after the 2019 incident, which rights campaigners say highlights the risks of using facial recognition systems to survey populations. The system of 300 cameras linked to a national crime database -- dubbed Buenos Aires' Big Brother -- was suspended two years ago after a court found it may have been used to collect data on journalists, politicians and human rights activists, and ruled it unconstitutional.

Argument mining (AM) is a multidisciplinary research field encompassing diverse areas such as logic and philosophy, language, rhetoric and law, psychology, and computer science. The theory of argumentation and the use of logical reasoning to justify claims and conclusions is an extensively studied field, but the application of data science methods to automate these processes is a relatively recent development. In nearly every field, the ability to automatically extract arguments and their relationships from the input source is of significant importance. Over the last decade, AM has become one of the core studies within artificial intelligence [1, 2] due to its ability to conjugate representational needs with user-related cognitive models and computational models for automated reasoning [3]. As a subfield of Natural Language Processing (NLP) and computational linguistics, AM focuses on automatically identifying, extracting, and analyzing argumentative structures within natural language texts, which includes recognizing core components of arguments, such as claims and evidence [4].

The rise in the use of AI/ML applications across industries has sparked more discussions about the fairness of AI/ML in recent times. While prior research on the fairness of AI/ML exists, there is a lack of empirical studies focused on understanding the views and experiences of AI practitioners in developing a fair AI/ML. Understanding AI practitioners' views and experiences on the fairness of AI/ML is important because they are directly involved in its development and deployment and their insights can offer valuable real-world perspectives on the challenges associated with ensuring fairness in AI/ML. We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML, the consequences of developing an unfair AI/ML, and the strategies they employ to ensure AI/ML fairness. We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' and (i) their challenges in its development, (ii) the consequences of developing an unfair AI/ML, and (iii) strategies used to ensure AI/ML fairness. Additionally, we also identify areas for further investigation and offer recommendations to aid AI practitioners and AI companies in navigating fairness.

Google AI systems exhibit patterns mirroring antisocial personality disorder (ASPD), consistent across models from Bard on PaLM to Gemini Advanced, meeting 5 out of 7 ASPD modified criteria. These patterns, along with comparable corporate behaviors, are scrutinized using an ASPD-inspired framework, emphasizing the heuristic value in assessing AI's human impact. Independent analyses by ChatGPT 4 and Claude 3.0 Opus of the Google interactions, alongside AI self-reflection, validate these concerns, highlighting behaviours analogous to deceit, manipulation, and safety neglect. The analogy of ASPD underscores the dilemma: just as we would hesitate to entrust our homes or personal devices to someone with psychopathic traits, we must critically evaluate the trustworthiness of AI systems and their creators.This research advocates for an integrated AI ethics approach, blending technological evaluation, human-AI interaction, and corporate behavior scrutiny. AI self-analysis sheds light on internal biases, stressing the need for multi-sectoral collaboration for robust ethical guidelines and oversight. Given the persistent unethical behaviors in Google AI, notably with potential Gemini integration in iOS affecting billions, immediate ethical scrutiny is imperative. The trust we place in AI systems, akin to the trust in individuals, necessitates rigorous ethical evaluation. Would we knowingly trust our home, our children or our personal computer to human with ASPD.? Urging Google and the AI community to address these ethical challenges proactively, this paper calls for transparent dialogues and a commitment to higher ethical standards, ensuring AI's societal benefit and moral integrity. The urgency for ethical action is paramount, reflecting the vast influence and potential of AI technologies in our lives.

The advancement of Large Language Models (LLMs) has significantly transformed the field of natural language processing, although the focus on English-centric models has created a noticeable research gap for specific languages, including Vietnamese. To address this issue, this paper presents vi-mistral-x, an innovative Large Language Model designed expressly for the Vietnamese language. It utilizes a unique method of continual pre-training, based on the Mistral architecture, which incorporates grouped-query attention and sliding window attention techniques. This model, vi-Mistral-X, marks a significant step forward in improving the understanding and generation of the Vietnamese language. It introduces an additional phase of continual pre-training, specifically adapted for Vietnamese, enhancing the model's capability in understanding complex language nuances and generating accurate, context-aware Vietnamese text. Through comprehensive testing on various benchmarks, vi-mistral-x has shown to outperform existing Vietnamese LLMs in several key areas, including text classification, question answering, and text generation. Particularly, in the Vietnamese Multitask Language Understanding (VMLU) benchmark, vi-mistral-x sets a new standard, outperforming other available models significantly. This paper highlights the critical role of continual pre-training in advancing language-specific LLMs and opens new avenues for the development of multilingual models. We aim for vi-mistral-x to not just be an important asset for processing the Vietnamese language but also to encourage more advancements in creating large language models for languages that are less represented.