In his polarizing "Techno-Optimist Manifesto" last year, venture capitalist Marc Andreessen listed a number of enemies to technological progress. Among them were "tech ethics" and "trust and safety," a term used for work on online content moderation, which he said had been used to subject humanity to "a mass demoralization campaign" against new technologies such as artificial intelligence. Andreessen's declaration drew both public and quiet criticism from people working in those fields--including at Meta, where Andreessen is a board member. Critics saw his screed as misrepresenting their work to keep internet services safer. On Wednesday, Andreessen offered some clarification: When it comes to his 9-year-old son's online life, he's in favor of guardrails.

Investors led by Microsoft pressured OpenAI to reinstate Altman, which it did within days, alongside vague promises to be more responsible. Then, last month, the company disbanded the internal group tasked with safety research, known as the "superalignment team." Some of the team's most prominent members publicly resigned, including its head, Jan Leike, who posted on X that "over the past years, safety culture and processes have taken a backseat to shiny products." Fortune reported that OpenAI did not provide anywhere near the resources it had initially, publicly promised for safety research. Saunders, who also worked on superalignment, said he resigned when he "lost hope a few months before Jan did."

Last week, an AI Overview search result from Google used one of my WIRED articles in an unexpected way that makes me fearful for the future of journalism. I was experimenting with AI Overviews, the company's new generative AI feature designed to answer online queries. I asked it multiple questions about topics I've recently covered, so I wasn't shocked to see my article linked, as a footnote, way at the bottom of the box containing the answer to my query. But I was caught off guard by how much the first paragraph of an AI Overview pulled directly from my writing. The following screenshot on the left is from an interview I conducted with one of Anthropic's product developers about tips for using the company's Claude chatbot.

Prior research demonstrates that Open AI's GPT models can predict variations in moral opinions between countries but that the accuracy tends to be substantially higher among high-income countries compared to low-income ones. This study aims to replicate previous findings and advance the research by examining how accuracy varies with different types of moral questions. Using responses from the World Value Survey and the European Value Study, covering 18 moral issues across 63 countries, we calculated country-level mean scores for each moral issue and compared them with GPT-4's predictions. Confirming previous findings, our results show that GPT-4 has greater predictive success in high-income than in low-income countries. However, our factor analysis reveals that GPT-4 bases its predictions primarily on a single dimension, presumably reflecting countries' degree of conservatism/liberalism. Conversely, the real-world moral landscape appears to be two-dimensional, differentiating between personal-sexual and violent-dishonest issues. When moral issues are categorized based on their moral domain, GPT-4's predictions are found to be remarkably accurate in the personal-sexual domain, across both high-income (r = .77) and low-income (r = .58) countries. Yet the predictive accuracy significantly drops in the violent-dishonest domain for both high-income (r = .30) and low-income (r = -.16) countries, indicating that GPT-4's one-dimensional world-view does not fully capture the complexity of the moral landscape. In sum, this study underscores the importance of not only considering country-specific characteristics to understand GPT-4's moral understanding, but also the characteristics of the moral issues at hand.

The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature. However, when facing a legal case, users without a legal background often struggle to formulate professional queries and may inadvertently overlook critical legal factors when presenting their case narrative to LLMs. To address this issue, we propose the Diagnostic Legal Large Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-quality feedback. D3LM incorporates an innovative graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm, enabling the generation of critical questions and enhancing user-LLM interactions. Moreover, an integrated LLM-based stopping criterion facilitates precise Court Views Generation (CVG). Our research also introduces a new English-language CVG dataset based on the US case law database, enriching the realm of LLM research and deployment with a vital dimension. D3LM surpasses classical LLMs by delivering outstanding performance and a remarkable user experience in the legal domain.

Advanced Air Mobility (AAM) is a growing field that demands a deep understanding of legal, spatial and temporal concepts in navigation. Hence, any implementation of AAM is forced to deal with the inherent uncertainties of human-inhabited spaces. Enabling growth and innovation requires the creation of a system for safe and robust mission design, i.e., the way we formalize intentions and decide their execution as trajectories for the Unmanned Aerial Vehicle (UAV). Although legal frameworks have emerged to govern urban air spaces, their full integration into the decision process of autonomous agents and operators remains an open task. In this work we present ProMis, a system architecture for probabilistic mission design. It links the data available from various static and dynamic data sources with legal text and operator requirements by following principles of formal verification and probabilistic modeling. Hereby, ProMis enables the combination of low-level perception and high-level rules in AAM to infer validity over the UAV's state-space. To this end, we employ Hybrid Probabilistic Logic Programs (HPLP) as a unifying, intermediate representation between perception and action-taking. Furthermore, we present methods to connect ProMis with crowd-sourced map data by generating HPLP atoms that represent spatial relations in a probabilistic fashion. Our claims of the utility and generality of ProMis are supported by experiments on a diverse set of scenarios and a discussion of the computational demands associated with probabilistic missions.

Machine unlearning provides viable solutions to revoke the effect of certain training data on pre-trained model parameters. Existing approaches provide unlearning recipes for classification and generative models. However, a category of important machine learning models, i.e., contrastive learning (CL) methods, is overlooked. In this paper, we fill this gap by first proposing the framework of Machine Unlearning for Contrastive learning (MUC) and adapting existing methods. Furthermore, we observe that several methods are mediocre unlearners and existing auditing tools may not be sufficient for data owners to validate the unlearning effects in contrastive learning. We thus propose a novel method called Alignment Calibration (AC) by explicitly considering the properties of contrastive learning and optimizing towards novel auditing metrics to easily verify unlearning. We empirically compare AC with baseline methods on SimCLR, MoCo and CLIP. We observe that AC addresses drawbacks of existing methods: (1) achieving state-of-the-art performance and approximating exact unlearning (retraining); (2) allowing data owners to clearly visualize the effect caused by unlearning through black-box auditing.

Task-oriented queries (e.g., one-shot queries to play videos, order food, or call a taxi) are crucial for assessing the quality of virtual assistants, chatbots, and other large language model (LLM)-based services. However, a standard benchmark for task-oriented queries is not yet available, as existing benchmarks in the relevant NLP (Natural Language Processing) fields have primarily focused on task-oriented dialogues. Thus, we present a new methodology for efficiently generating the Task-oriented Queries Benchmark (ToQB) using existing task-oriented dialogue datasets and an LLM service. Our methodology involves formulating the underlying NLP task to summarize the original intent of a speaker in each dialogue, detailing the key steps to perform the devised NLP task using an LLM service, and outlining a framework for automating a major part of the benchmark generation process. Through a case study encompassing three domains (i.e., two single-task domains and one multi-task domain), we demonstrate how to customize the LLM prompts (e.g., omitting system utterances or speaker labels) for those three domains and characterize the generated task-oriented queries. The generated ToQB dataset is made available to the public. We further discuss new domains that can be added to ToQB by community contributors and its practical applications.

Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research of new ML algorithms, and contributing to the democratization of ML by making it accessible to a broader audience. Over the past decade, commendable achievements in AutoML have primarily focused on optimizing predictive performance. This focused progress, while substantial, raises questions about how well AutoML has met its broader, original goals. In this position paper, we argue that a key to unlocking AutoML's full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems, including their diverse roles, expectations, and expertise. We envision a more human-centered approach in future AutoML research, promoting the collaborative design of ML systems that tightly integrates the complementary strengths of human expertise and AutoML methodologies.

Existing group fairness notions require algorithms to treat Fairness has become a crucial aspect in the development different groups equally, and the degree of fairness violation of trustworthy machine learning algorithms. is usually measured via the dissimilarity of model Current fairness metrics to measure predictions. For example, Demographic Parity (DP) requires the violation of demographic parity have the following model predictions to be independent of sensitive attributes drawbacks: (i) the average difference of (Dwork et al., 2012; Kamishima et al., 2012; Jiang model predictions on two groups cannot reflect et al., 2020). To measure the violation of DP, most of existing their distribution disparity, and (ii) the overall calculation works adopt DP metric, which calculates the difference along all possible predictions conceals in average predictions between the two demographic the extreme local disparity at or around certain groups (Zemel et al., 2013; Chuang & Mroueh, 2021; Li predictions. In this work, we propose a novel et al., 2023b). However, since having the same values in fairness metric called Maximal Cumulative ratio average predictions between the two groups cannot ensure Disparity along varying Predictions' neighborhood that the distributions are also the same, we argue that the (MCDP), for measuring the maximal local widely used DP may fail to detect the violation of demographic disparity of the fairness-aware classifiers.