An influential California legislator is pressuring the State Bar of California to ditch its new multiple-choice questions after a February bar exam debacle and revert to the traditional test format in July. "Given the catastrophe of the February bar, I think that going back to the methods that have been used for the last 50 years -- until we can adequately test what new methods may be employed -- is the appropriate way to go," Sen. Tom Umberg (D-Orange), chair of the state Senate Judiciary Committee, told The Times. Thousands of test takers seeking to practice law in California typically take the two-day bar exam in July. Reverting to the national system by the National Conference of Bar Examiners, which California has used since 1972, would be a major retreat for the embattled State Bar. Its new exam was rolled out this year as a cost-cutting measure and "historic agreement" that would offer test takers the choice of remote testing.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. "Free speech" was the battering ram that Elon Musk used to justify his pursuit of Twitter in 2022. He talked about the platform as the new digital town square. He said social media companies' moderation policies should be no more restrictive than national laws. "I hope that even my worst critics remain on Twitter, because that is what free speech means," he wrote after agreeing to a 44 billion takeover. In the three years since making the deal, Musk has continued to cloak himself in the armor of a free speech warrior, out there fighting for the rest of us.

Google has a monopoly, and that's the official line of the US federal government. In fact, it has two of them, losing two separate antitrust cases that threaten to cripple the tech giant. The Department of Justice has proposed forcing Google to sell or otherwise divest itself of the Chrome browser as its first and preferred remedy. But who would buy it? Unsurprisingly, there are beaucoup business beaus lining up around the block for this browser bachelorette.

Elon Musk's artificial intelligence company is stirring controversy in Memphis, Tennessee. That's where he's building a massive supercomputer to power his company xAI. Community residents and environmental activists say that since the supercomputer was fired up last summer it has become one of the biggest air polluters in the county. But some local officials have championed the billionaire, saying he's investing in Memphis. The first public hearing with the health department is scheduled for Friday, where county officials will hear from all sides of the debate.

The House of Representatives, Japan's lower chamber of parliament, passed a bill on Thursday to promote the development of artificial intelligence technology and take steps to mitigate its risks. The legislation is expected to be enacted during the current parliamentary session set to end in June after deliberations at the House of Councilors, the upper chamber. AI "will be the foundation of economic and social development and is an important technology from the viewpoint of security," the bill said.

In this paper, we adopt a survivor-centered approach to locate and dissect the role of sociotechnical AI governance in preventing AI-Generated Non-Consensual Intimate Images (AIG-NCII) of adults, colloquially known as "deep fake pornography." We identify a "malicious technical ecosystem" or "MTE," comprising of open-source face-swapping models and nearly 200 "nudifying" software programs that allow non-technical users to create AIG-NCII within minutes. Then, using the National Institute of Standards and Technology (NIST) AI 100-4 report as a reflection of current synthetic content governance methods, we show how the current landscape of practices fails to effectively regulate the MTE for adult AIG-NCII, as well as flawed assumptions explaining these gaps.

The proliferation of abusive language in online communications has posed significant risks to the health and wellbeing of individuals and communities. The growing concern regarding online abuse and its consequences necessitates methods for identifying and mitigating harmful content and facilitating continuous monitoring, moderation, and early intervention. This paper presents a taxonomy for distinguishing key characteristics of abusive language within online text. Our approach uses a systematic method for taxonomy development, integrating classification systems of 18 existing multi-label datasets to capture key characteristics relevant to online abusive language classification. The resulting taxonomy is hierarchical and faceted, comprising 5 categories and 17 dimensions. It classifies various facets of online abuse, including context, target, intensity, directness, and theme of abuse. This shared understanding can lead to more cohesive efforts, facilitate knowledge exchange, and accelerate progress in the field of online abuse detection and mitigation among researchers, policy makers, online platform owners, and other stakeholders.

As generative AI models become increasingly integrated into high-stakes domains, the need for robust methods to evaluate their ethical reasoning becomes increasingly important. This paper introduces a five-dimensional audit model -- assessing Analytic Quality, Breadth of Ethical Considerations, Depth of Explanation, Consistency, and Decisiveness -- to evaluate the ethical logic of leading large language models (LLMs). Drawing on traditions from applied ethics and higher-order thinking, we present a multi-battery prompt approach, including novel ethical dilemmas, to probe the models' reasoning across diverse contexts. We benchmark seven major LLMs finding that while models generally converge on ethical decisions, they vary in explanatory rigor and moral prioritization. Chain-of-Thought prompting and reasoning-optimized models significantly enhance performance on our audit metrics. This study introduces a scalable methodology for ethical benchmarking of AI systems and highlights the potential for AI to complement human moral reasoning in complex decision-making contexts.

Unsupervised machine learning techniques, such as topic modeling and clustering, are often used to identify latent patterns in unstructured text data in fields such as political science and sociology. These methods overcome common concerns about reproducibility and costliness involved in the labor-intensive process of human qualitative analysis. However, two major limitations of topic models are their interpretability and their practicality for answering targeted, domain-specific social science research questions. In this work, we investigate opportunities for using LLM-generated text augmentation to improve the usefulness of topic modeling output. We use a political science case study to evaluate our results in a domain-specific application, and find that topic modeling using GPT-4 augmentations creates highly interpretable categories that can be used to investigate domain-specific research questions with minimal human guidance.

Large language models (LLMs) have demonstrated remarkable capabilities, but they require vast amounts of data and computational resources. In contrast, smaller models (SMs), while less powerful, can be more efficient and tailored to specific domains. In this position paper, we argue that taking a collaborative approach, where large and small models work synergistically, can accelerate the adaptation of LLMs to private domains and unlock new potential in AI. We explore various strategies for model collaboration and identify potential challenges and opportunities. Building upon this, we advocate for industry-driven research that prioritizes multi-objective benchmarks on real-world private datasets and applications.