This article presents an exploration of the use of pre-trained Large Language Models (LLMs) for crisis classification to address labeled data dependency issues. We present a methodology that enhances open LLMs through fine-tuning, creating zero-shot and few-shot classifiers that approach traditional supervised models in classifying crisis-related messages. A comparative study evaluates crisis classification tasks using general domain pre-trained LLMs, crisis-specific LLMs, and traditional supervised learning methods, establishing a benchmark in the field. Our task-specific fine-tuned Llama model achieved a 69% macro F1 score in classifying humanitarian information–a remarkable 26% improvement compared to the Llama baseline, even with limited training data. Moreover, it outperformed ChatGPT4 by 3% in macro F1. This improvement increased to 71% macro F1 when fine-tuning Llama with multitask data. For the binary classification of messages as related vs. not related to crises, we observed that pre-trained LLMs, such as Llama 2 and ChatGPT4, performed well without fine-tuning, achieving an 87% macro F1 score with ChatGPT4. This research expands our knowledge of how to exploit the potential of LLMs for crisis classification, representing a great opportunity for crisis scenarios that lack labeled data. The findings emphasize the potential of LLMs in crisis informatics to address cold start challenges, especially critical in the initial phases of a disaster, while also showcasing their capacity to attain high accuracy even with limited training data.

Social media data has emerged as a useful source of timely information about real-world crisis events. One of the main tasks related to the use of social media for disaster management is the automatic identification of crisis-related messages. Most of the studies on this topic have focused on the analysis of data for a particular type of event in a specific language. This limits the possibility of generalizing existing approaches because models cannot be directly applied to new types of events or other languages. In this work, we study the task of automatically classifying messages that are related to crisis events by leveraging cross-language and cross-domain labeled data. Our goal is to make use of labeled data from high-resource languages to classify messages from other (low-resource) languages and/or of new (previously unseen) types of crisis situations. For our study we consolidated from the literature a large unified dataset containing multiple crisis events and languages. Our empirical findings show that it is indeed possible to leverage data from crisis events in English to classify the same type of event in other languages, such as Spanish and Italian (80.0% F1-score). Furthermore, we achieve good performance for the cross-domain task (80.0% F1-score) in a cross-lingual setting. Overall, our work contributes to improving the data scarcity problem that is so important for multilingual crisis classification. In particular, mitigating cold-start situations in emergency events, when time is of essence.

Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potentially catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that total containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) impossible. This article is part of the special track on AI and Society.

Recommendation systems today exert a strong influence on consumer behavior and individual perceptions of the world. By using collaborative filtering (CF) methods to create recommendations, it generates a continuous feedback loop in which user behavior becomes magnified in the algorithmic system. Popular items get recommended more frequently, creating the bias that affects and alters user preferences. In order to visualize and compare the different biases, we will analyze the effects of recommendation systems and quantify the inequalities resulting from them.

Different social media environments enable different levels of community connectedness, which in turn affects the information that a user is exposed to. In this study, we create an agent-based model to investigate how the different levels of connectedness as well as network structure affects a group's diversity of opinions. In the model, agents are tasked with “liking” or “disliking” a set of objects. At each turn, each agent sees the most popular object amongst the agents that they are connected to. There are two main findings: 1) low network connectivity leads to more diversity amongst agents, 2) a complete network leads information popularity distribution to be more skewed, 3) the more random a network is, the more skewed information popularity distribution is. These findings suggest that online platforms that either create well-connected communities or present aggregated information of users (such as Billboard rankings) may lead to homogeneity in the subjective preference of the users over time.

Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown that forecasts can be further improved by ``hybridizing'' human forecasters: pairing them with the machine models in an effort to combine the unique advantages of both. In this demonstration, we present Synergistic Anticipation of Geopolitical Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models.

How does the number of collaborators affect individual productivity? Results of prior research have been conflicting, with some studies reporting an increase in individual productivity as the number of collaborators grows, while other studies showing that the free-rider effect skews the effort invested by individuals, making larger groups less productive. The difference between these schools of thought is substantial: if a super-scaling effect exists, as suggested by former studies, then as groups grow, their productivity will increase even faster than their size, super-linearly improving their efficiency. We address this question by studying two planetary-scale collaborative systems: GitHub and Wikipedia. By analyzing the activity of over 2 million users on these platforms, we discover that the interplay between group size and productivity exhibits complex, previously-unobserved dynamics: the productivity of smaller groups scales super-linearly with group size, but saturates at larger sizes. This effect is not an artifact of the heterogeneity of productivity: the relation between group size and productivity holds at the individual level. People tend to do more when collaborating with more people. We propose a generative model of individual productivity that captures the non-linearity in collaboration effort. The proposed model is able to explain and predict group work dynamics in GitHub and Wikipedia by capturing their maximally informative behavioral features, and it paves the way for a principled, data-driven science of collaboration.

We introduce a computational model for building moral autonomous vehicles by learning and generalizing from human moral judgments. We draw on a cognitively inspired model of how people and young children learn moral theories from sparse and noisy data and integrate observations made from different people in different groups. The problem of moral learning for autonomous vehicles is cast as learning how to weigh the different features of the dilemma using utility calculus, with the goal of making these trade-offs reflect how people make them in a wide variety of moral dilemma. By modeling the structures of individuals and groups in a hierarchical Bayesian model, we show that an individual's moral values -- as well as a group's shared values -- can be inferred from sparse and noisy data. We evaluate our approach with data from the Moral Machine, a web application that collects human judgments on moral dilemmas involving autonomous vehicles, and show that the model rapidly and accurately infers people's preferences and can predict the difficulty of moral dilemmas from limited data.

Unpredictability is often portrayed as an undesirable outcome of social influence in cultural markets. Unpredictability stems from the "rich get richer" effect, whereby small fluctuations in the market share or popularity of products are amplified over time by social influence. In this paper, we report results of an experimental study that shows that unpredictability is not an inherent property of social influence. We investigate strategies for creating markets in which the popularity of products is better-and more predictably-aligned with their underlying quality. For our study, we created a cultural market of science stories and conducted randomized experiments on different policies for presenting the stories to study participants. Specifically, we varied how the stories were ranked, and whether or not participants were shown the ratings these stories received from others. We present a policy that leverages social influence and product positioning to help distinguish the product's market share (popularity) from underlying quality. Highlighting products with the highest estimated quality reduces the "rich get richer" effect highlighting popular products. We show that this policy allows us to more robustly and predictably identify high quality products and promote blockbusters. The policy can be used to create more efficient online cultural markets with a better allocation of resources to products.