Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of humans. However, internalizing such safeguard features into larger models brought challenges of higher training cost and unintended degradation of helpfulness. To overcome such challenges, a modular approach employing a smaller LLM to detect harmful user queries is regarded as a convenient solution in designing LLM-based system with safety requirements. In this paper, we leverage a smaller LLM for both harmful query detection and safeguard response generation. We introduce our safety requirements and the taxonomy of harmfulness categories, and then propose a multi-task learning mechanism fusing the two tasks into a single model. We demonstrate the effectiveness of our approach, providing on par or surpassing harmful query detection and safeguard response performance compared to the publicly available LLMs.

This study investigates the concept of the `right to be forgotten' within the context of large language models (LLMs). We explore machine unlearning as a pivotal solution, with a focus on pre-trained models--a notably under-researched area. Our research delineates a comprehensive framework for machine unlearning in pre-trained LLMs, encompassing a critical analysis of seven diverse unlearning methods. Through rigorous evaluation using curated datasets from arXiv, books, and GitHub, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over $10^5$ times more computationally efficient than retraining. Our results show that integrating gradient ascent with gradient descent on in-distribution data improves hyperparameter robustness. We also provide detailed guidelines for efficient hyperparameter tuning in the unlearning process. Our findings advance the discourse on ethical AI practices, offering substantive insights into the mechanics of machine unlearning for pre-trained LLMs and underscoring the potential for responsible AI development.

In many prediction problems, the predictive model affects the distribution of the prediction target. This phenomenon is known as performativity and is often caused by the behavior of individuals with vested interests in the outcome of the predictive model. Although performativity is generally problematic because it manifests as distribution shifts, we develop algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees in social classification problems (compared to what is achievable in non-performative settings). In particular, we leverage the policymaker's ability to steer the population to remedy inequities in the long term. A crucial benefit of this approach is that it is possible to resolve the incompatibilities between conflicting group fairness definitions.

Researchers have suggested giving algorithms power over one of the most crucial backbones of American society - the justice system. Scientists from MIT proposed the tech could be used to make pre-trial bail decisions fairer after their study found human judges are systematically biased. The team analyzed more than one million cases in New York City, finding 20 percent of judges made their conclusions based on the defendant's age, race or criminal history. The paper found that decisions of at least 32 percent of judges were inconsistent with the actual ability of defendants to post a specified bail amount and real the risk of them failing to appear for trial. A new paper found that New York judges sometimes made a mistake based on their own biases when setting bail for a new defendant.

Elon Musk, owner of Tesla and the X (formerly Twitter) platform, attends a symposium on fighting antisemitism titled'Never Again: Lip Service or Deep Conversation' in Krakow, Poland, on Jan. 22, 2024. SHOW ME THE MONEY: Billionaire entrepreneur Elon Musk reiterated his stance this week that artificial intelligence will eventually eliminate the need for humans to work, giving his vision for how the future will look as the technology continues to rapidly advance. AI IN POLITICAL ADS: The Federal Communications Commission last week proposed a new regulation that would require the use of artificial intelligence in political advertisements to be disclosed, which has one commissioner slamming the move as regulatory overreach ahead of the election. The Eastern Command of the Indian Army is currently showcasing the latest defense artillery robot at a stall during'East Tech 2023' in Guwahati, Assam, India, on October 10, 2023. HI-TECH WAR PLANNING: India, a country blessed with a strong high-tech industry, is applying its brains not just to commercial artificial intelligence but also to its military, as its neighbor and regional rival China continues to pour billions into AI research.

Police seized ketamine hidden inside life-size Transformer robots in Thailand. A woman who was previously caught trying to ship meth hidden in a food processing machine was trying to send the robots to Taiwan. Thai prosecutors said Wednesday former Prime Minister Thaksin Shinawatra will be indicted for defaming the monarchy, three months after he was freed on parole on other charges. Thaksin will not yet be indicted because he had filed a request to postpone his original appointment on Wednesday with proof that he has COVID-19, Prayuth Bejraguna, a spokesperson for the Office of the Attorney General, said at a news conference. The attorney general's office scheduled a new appointment for Thaksin's indictment on June 18, Prayuth said, adding that Thaksin will also be indicted for violating the Computer Crime Act.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we learn about causality and natural language, find out about the grassroots initiative AfriClimate AI, and discuss what responsible and trustworthy AI really means. In a series of interviews, we're chatting to some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We caught up with Salena Torres Ashton and found out about her work focusing on causality and natural language. Salena was a professional genealogist and historian for 25 years before deciding to return to University and study for a PhD.

The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI's impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year's edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Student data, which is a critical component in EDM research, can contain personal information, such as age and gender, as well as academic performance and activity data from online learning systems [24]. By offering valuable insights into student learning, EDM supports the development of more effective educational practices and policies, ultimately improving student outcomes. One of the most popular techniques in the previous works is incorporating machine learning techniques, which has achieved remarkable success in discovering intricate structures within educational datasets. However, in recent years, concerns about the fairness of deploying algorithmic decision-making in the educational context have emerged [2, 22, 27, 49]. Particularly, machine learning models can produce biased and unfair outcomes for certain student groups, significantly affecting their educational opportunities and achievements. Given that the data empowering EDM research can often contain personally identifiable and other sensitive information, there has been increased attention to privacy protection in recent years [37, 43]. Additionally, privacy legislation such as the California Consumer Privacy Act [39] and the former Right to be Forgotten [17] has granted users the right to erase the impact of their sensitive information from the trained models to protect their privacy. One approach to protecting users' privacy involves enabling the trained machine learning model to entirely forget Both authors contributed equally to this research.

Evaluating startups in their early stages is a complex task that requires detailed analysis by experts. While automating this process on a large scale can significantly impact businesses, the inherent complexity poses challenges. This paper addresses this challenge by introducing the Startup Success Forecasting Framework (SSFF), a new automated system that combines traditional machine learning with advanced language models. This intelligent agent-based architecture is designed to reason, act, synthesize, and decide like a venture capitalist to perform the analysis end-to-end. The SSFF is made up of three main parts: - Prediction Block: Uses random forests and neural networks to make predictions. - Analyst Block: Simulates VC analysis scenario and uses SOTA prompting techniques - External Knowledge Block: Gathers real-time information from external sources. This framework requires minimal input data about the founder and startup description, enhances it with additional data from external resources, and performs a detailed analysis with high accuracy, all in an automated manner