While Large Language Models (LLMs) have been increasingly used to generate synthetic data for various downstream tasks, researchers overlook the causal direction in the data synthesis process. A natural causal direction should contain two steps: diverse raw data are generated first, and subsequently annotated for downstream tasks. However, most LLM-based methods adopt an anticausal direction: embedding label information in the prompt to force LLMs to generate targeted data. This reversal raises a critical question: How does the direction of data synthesis impact the quality and utility of the synthetic data? In this work, we empirically study the impact of causal and anticausal data synthesis. To do so, we first design simple yet effective prompting strategies to control the causal direction of LLM-based data synthesis. Using GPT-5 as the data generator, we construct synthetic datasets for three distinct machine learning tasks. We then fine-tune BERT-base and LLaMA-3.2-1B models on these datasets and evaluate them against human-curated benchmarks. Our experiments reveal consistent patterns: (1) models trained on anticausal synthetic data suffer larger performance drops across all tasks and model families --- Accuracy declines range from 13.7%-59.1% for BERT and 4.9%-54.3% for LLaMA, and (2) distributional analysis shows that anticausal synthetic datasets deviate further from human data. Our findings provide practical guidance on how to generate better synthetic data and make good use of it.

Traditional disaster analysis and modelling tools for assessing the severity of a disaster are predictive in nature. Based on the past observational data, these tools prescribe how the current input state (e.g., environmental conditions, situation reports) results in a severity assessment. However, these systems are not meant to be interventional in the causal sense, where the user can modify the current input state to simulate counterfactual ''what-if'' scenarios. In this work, we provide an alternative interventional tool that complements traditional disaster modelling tools by leveraging real-time data sources like satellite imagery, news, and social media. Our tool also helps understand the causal attribution of different factors on the estimated severity, over any given region of interest. In addition, we provide actionable recourses that would enable easier mitigation planning. Our source code is publicly available.

A Convolutional Neural Network (CNN) relies on intricate pixel correlations to predict objects within images, demonstrating improved performance when identifying objects alongside commonly co-occurring objects. For instance, a helicopter can be more accurately predicted when it appears with an aircraft carrier compared to when it stands alone. Influence on detecting an object due to the presence or absence of another object can be regarded as a form of contextual bias. This phenomenon poses a significant challenge in object detection. Addressing this bias is crucial. Our study explores correlation-aware prediction techniques to disentangle and leverage connections between objects. We propose CART-OD, a novel object detection approach that enhances model performance by adjusting the confidence scores of correlated objects during inference. By strategically intervening during prediction, CART-OD targets instances where commonly co-occurring objects appear separately, effectively mitigating the influence of object correlation. The adjustment in real-time makes the approach computationally efficient and scalable to handle large-scale datasets. Results indicate that CART-OD significantly enhances precision, recall, and mean average precision (mAP) in detecting objects that appear without their most correlated counterparts, underscoring the potential of correlation-aware strategies in object detection tasks.

The evergrowing reliance of humans and society on machine learning methods has raised concerns about their trustworthiness and liability. As a response to these concerns, Socially Responsible Machine Learning (SRML) aims at developing fair, transparent, and robust machine learning algorithms. However, traditional approaches to SRML do not incorporate human perspectives, and therefore are not sufficient to build long-lasting trust between machines and human being. Causality as the key to human intelligence plays a vital role in achieving socially responsible machine learning algorithms which are compatible with human notions. Bridging the gap between traditional SRML and causality, in this tutorial, we aim at providing a holistic overview of SRML through the lens of causality. In particular, we will focus on state-of-the-art techniques on causal socially responsible ML in terms of fairness, interpretability, and robustness. The objectives of this tutorial are as follows: (1) we provide a taxonomy of existing literature on causal socially responsible ML from fairness, interpretability, and robustness perspective; (2) we review the state-of-the-art techniques for each task; and (3) we elucidate open questions and future research directions. We believe this tutorial is beneficial to researchers and practitioners from the areas of data mining, machine learning, and social sciences.

Information has a dire role when it comes to decision-making, especially in complex multi-domain operational environments. While it can be assumed that obtaining more information is more beneficial, it can lead towards several challenges. For a decision maker, to collect and assess large quantities of information in order to make an informed decision can be a difficult task. The decision-making task becomes more problematic due to the imperfect nature of information expressed through the uncertainty of information (UoI) concept. This paper proposes implementing multi-criteria decision making (MCDM) within a dynamic multi-agent-based framework called Sentry Agents (SAGE), which allows for the simulation of various decision-making scenarios and diverse decision maker behaviors. We evaluate how MCDM can measure interdependence between criteria, and how that impacts the determination of an ideal course of action in a decision-making task.