This is part of Help! Wanted, a special series from Slate advice. In the advising biz, there are certain eternal dilemmas that bedevil letter writers and columnists alike. For this edition, we asked writer Sable Yong to field your questions about online dating. She writes the newsletter Hard Feelings and her first essay collection Die Hot With A Vengeance will be published by Harper Collins in 2024. Matched is a pop-up advice column about online dating. Have a question about navigating dating apps? Can you make a ruling once and for all: If I'm on an app like Tinder or Grindr, and it clearly states I am there for "short-term fun" or "right now," do I really need to also talk about being in an open relationship with potential partners?

Similar Case Matching (SCM) plays a pivotal role in the legal system by facilitating the efficient identification of similar cases for legal professionals. While previous research has primarily concentrated on enhancing the performance of SCM models, the aspect of interpretability has been neglected. To bridge the gap, this study proposes an integrated pipeline framework for interpretable SCM. The framework comprises four modules: judicial feature sentence identification, case matching, feature sentence alignment, and conflict resolution. In contrast to current SCM methods, our framework first extracts feature sentences within a legal case that contain essential information. Then it conducts case matching based on these extracted features. Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity. In instances where the results of case matching and feature sentence alignment exhibit conflicts, the conflict resolution module resolves these inconsistencies. The experimental results show the effectiveness of our proposed framework, establishing a new benchmark for interpretable SCM.

Generative AI, in particular text-based "foundation models" (large models trained on a huge variety of information including the internet), can generate speech that could be problematic under a wide range of liability regimes. Machine learning practitioners regularly "red team" models to identify and mitigate such problematic speech: from "hallucinations" falsely accusing people of serious misconduct to recipes for constructing an atomic bomb. A key question is whether these red-teamed behaviors actually present any liability risk for model creators and deployers under U.S. law, incentivizing investments in safety mechanisms. We examine three liability regimes, tying them to common examples of red-teamed model behaviors: defamation, speech integral to criminal conduct, and wrongful death. We find that any Section 230 immunity analysis or downstream liability analysis is intimately wrapped up in the technical details of algorithm design. And there are many roadblocks to truly finding models (and their associated parties) liable for generated speech. We argue that AI should not be categorically immune from liability in these scenarios and that as courts grapple with the already fine-grained complexities of platform algorithms, the technical details of generative AI loom above with thornier questions. Courts and policymakers should think carefully about what technical design incentives they create as they evaluate these issues.

Porcha Woodruff was eight months pregnant when police in Detroit, Michigan came to arrest her on charges of carjacking and robbery. She was getting her two children ready for school when six police officers knocked on her door and presented her with an arrest warrant. She thought it was a prank. Do you see that I am eight months pregnant?" the lawsuit Woodruff filed against Detroit police reads. She sent her children upstairs to tell her fiance that "Mommy's going to jail". She was detained and questioned for 11 hours and released on a $100,000 bond. She immediately went to the hospital, where she was treated for dehydration. Woodruff later found out that she was the latest victim of false identification by facial recognition. After her image was incorrectly matched to video footage of a woman at the gas station where the carjacking took place, her picture was shown to the victim in a photo lineup. According to the lawsuit, the victim allegedly chose Woodruff's picture as the woman ...

Beijing is poised to implement sweeping new regulations for artificial intelligence services this week, trying to balance state control of the technology with enough support that its companies can become viable global competitors. The government has issued 24 guidelines that require platform providers to register their services and conduct a security review before they're brought to market. Seven agencies will take responsibility for oversight, including the Cyberspace Administration of China (CAC) and the National Development and Reform Commission. The final regulations are less onerous than an original draft from April, but they show that China, like Europe, is moving ahead with government oversight of what may be the most promising -- and controversial -- technology of the last 30 years.

Beijing is poised to implement sweeping new regulations for artificial intelligence services this week, trying to balance state control of the technology with enough support that its companies can become viable global competitors. The government issued 24 guidelines that require platform providers to register their services and conduct a security review before they're brought to market. Seven agencies will take responsibility for oversight, including the Cyberspace Administration of China and the National Development and Reform Commission. The final regulations are less onerous than an original draft from April, but they show China, like Europe, moving ahead with government oversight of what may be the most promising -- and controversial -- technology of the last 30 years. The U.S., by contrast, has no legislation under serious consideration even after industry leaders warned that AI poses a "risk of extinction" and OpenAI's Sam Altman urged Congress in public hearings to get involved.

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that encode censoring information in the neural network architecture. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, $r$-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2.5) concentration over the whole of Saudi Arabia.

When deploying machine learning models in production for any product/application, there are three properties that are commonly desired. First, the models should be generalizable, in that we can extend it to further use cases as our knowledge of the domain area develops. Second they should be evaluable, so that there are clear metrics for performance and the calculation of those metrics in production settings are feasible. Finally, the deployment should be cost-optimal as far as possible. In this paper we propose that these three objectives (i.e. generalization, evaluation and cost-optimality) can often be relatively orthogonal and that for large language models, despite their performance over conventional NLP models, enterprises need to carefully assess all the three factors before making substantial investments in this technology. We propose a framework for generalization, evaluation and cost-modeling specifically tailored to large language models, offering insights into the intricacies of development, deployment and management for these large language models.

Recent supervised models for event coding vastly outperform pattern-matching methods. However, their reliance solely on new annotations disregards the vast knowledge within expert databases, hindering their applicability to fine-grained classification. To address these limitations, we explore zero-shot approaches for political event ontology relation classification, by leveraging knowledge from established annotation codebooks. Our study encompasses both ChatGPT and a novel natural language inference (NLI) based approach named ZSP. ZSP adopts a tree-query framework that deconstructs the task into context, modality, and class disambiguation levels. This framework improves interpretability, efficiency, and adaptability to schema changes. By conducting extensive experiments on our newly curated datasets, we pinpoint the instability issues within ChatGPT and highlight the superior performance of ZSP. ZSP achieves an impressive 40% improvement in F1 score for fine-grained Rootcode classification. ZSP demonstrates competitive performance compared to supervised BERT models, positioning it as a valuable tool for event record validation and ontology development. Our work underscores the potential of leveraging transfer learning and existing expertise to enhance the efficiency and scalability of research in the field.

Land-use decision-making processes have a long history of producing globally pervasive systemic equity and sustainability concerns. Quantitative, optimization-based planning approaches, e.g. Multi-Objective Land Allocation (MOLA), seemingly open the possibility to improve objectivity and transparency by explicitly evaluating planning priorities by the type, amount, and location of land uses. Here, we show that optimization-based planning approaches with generic planning criteria generate a series of unstable "flashpoints" whereby tiny changes in planning priorities produce large-scale changes in the amount of land use by type. We give quantitative arguments that the flashpoints we uncover in MOLA models are examples of a more general family of instabilities that occur whenever planning accounts for factors that coordinate use on- and between-sites, regardless of whether these planning factors are formulated explicitly or implicitly. We show that instabilities lead to regions of ambiguity in land-use type that we term "gray areas". By directly mapping gray areas between flashpoints, we show that quantitative methods retain utility by reducing combinatorially large spaces of possible land-use patterns to a small, characteristic set that can engage stakeholders to arrive at more efficient and just outcomes.