Practical Guide for Causal Pathways and Sub-group Disparity Analysis

In this study, we introduce the application of causal disparity analysis to unveil intricate relationships and causal pathways between sensitive attributes and the targeted outcomes within real-world observational data. Our methodology involves employing causal decomposition analysis to quantify and examine the causal interplay between sensitive attributes and outcomes. We also emphasize the significance of integrating heterogeneity assessment in causal disparity analysis to gain deeper insights into the impact of sensitive attributes within specific sub-groups on outcomes. Our two-step investigation focuses on datasets where race serves as the sensitive attribute. The results on two datasets indicate the benefit of leveraging causal analysis and heterogeneity assessment not only for quantifying biases in the data but also for disentangling their influences on outcomes. We demonstrate that the sub-groups identified by our approach to be affected the most by disparities are the ones with the largest ML classification errors. We also show that grouping the data only based on a sensitive attribute is not enough, and through these analyses, we can find sub-groups that are directly affected by disparities. We hope that our findings will encourage the adoption of such methodologies in future ethical AI practices and bias audits, fostering a more equitable and fair technological landscape.

Developing Safe and Responsible Large Language Models -- A Comprehensive Framework

Given the growing concerns around the safety and risks of Large Language Models (LLMs), it is essential to develop methods for mitigating these issues. We introduce Safe and Responsible Large Language Model (SR$_{\text{LLM}}$) , a model designed to enhance the safety of language generation using LLMs. Our approach incorporates a comprehensive LLM safety risk taxonomy and utilizes a dataset annotated by experts that align with this taxonomy. SR$_{\text{LLM}}$ is designed to identify potentially unsafe content and produce benign variations. It employs instruction-based and parameter-efficient fine-tuning methods, making the model not only effective in enhancing safety but also resource-efficient and straightforward to adjust. Through our testing on five benchmark datasets and two proprietary datasets, we observed notable reductions in the generation of unsafe content. Moreover, following the implementation of safety measures, there was a significant improvement in the production of safe content. We detail our fine-tuning processes and how we benchmark safety for SR$_{\text{LLM}}$ with the community engagement and promote the responsible advancement of LLMs. All the data and code are available anonymous at https://github.com/shainarazavi/Safe-Responsible-LLM .

FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections

In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information. To tackle this challenge head-on, we introduce FakeWatch, a comprehensive framework carefully designed to detect fake news. Leveraging a newly curated dataset of North American election-related news articles, we construct robust classification models. Our framework integrates a model hub comprising of both traditional machine learning (ML) techniques and cutting-edge Language Models (LMs) to discern fake news effectively. Our overarching objective is to provide the research community with adaptable and precise classification models adept at identifying the ever-evolving landscape of misinformation. Quantitative evaluations of fake news classifiers on our dataset reveal that, while state-of-the-art LMs exhibit a slight edge over traditional ML models, classical models remain competitive due to their balance of accuracy and computational efficiency. Additionally, qualitative analyses shed light on patterns within fake news articles. This research lays the groundwork for future endeavors aimed at combating misinformation, particularly concerning electoral processes. We provide our labeled data and model publicly for use and reproducibility.

FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections

In today's technologically driven world, the spread of fake news, particularly during crucial events such as elections, presents an increasing challenge to the integrity of information. To address this challenge, we introduce FakeWatch ElectionShield, an innovative framework carefully designed to detect fake news. We have created a novel dataset of North American election-related news articles through a blend of advanced language models (LMs) and thorough human verification, for precision and relevance. We propose a model hub of LMs for identifying fake news. Our goal is to provide the research community with adaptable and accurate classification models in recognizing the dynamic nature of misinformation. Extensive evaluation of fake news classifiers on our dataset and a benchmark dataset shows our that while state-of-the-art LMs slightly outperform the traditional ML models, classical models are still competitive with their balance of accuracy, explainability, and computational efficiency. This research sets the foundation for future studies to address misinformation related to elections.

Design-Inclusive Language Models for Responsible Information Access

As the use of large language models (LLMs) increases for everyday tasks, appropriate safeguards must be in place to ensure unbiased and safe output. Recent events highlight ethical concerns around conventionally trained LLMs, leading to overall unsafe user experiences. This motivates the need for responsible LLMs that are trained fairly, transparent to the public, and regularly monitored after deployment. In this work, we introduce the "Responsible Development of Language Models (ReDev)" framework to foster the development of fair, safe, and robust LLMs for all users. We also present a test suite of unique prompt types to assess LLMs on the aforementioned elements, ensuring all generated responses are non-harmful and free from biased content. Outputs from four state-of-the-art LLMs, OPT, GPT-3.5, GPT-4, and LLaMA-2, are evaluated by our test suite, highlighting the importance of considering fairness, safety, and robustness at every stage of the machine learning pipeline, including data curation, training, and post-deployment.

Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis

Bias detection in text is imperative due to its role in reinforcing negative stereotypes, disseminating misinformation, and influencing decisions. Current language models often fall short in generalizing beyond their training sets. In response, we introduce the Contextualized Bi-Directional Dual Transformer (CBDT) Classifier. This novel architecture utilizes two synergistic transformer networks: the Context Transformer and the Entity Transformer, aiming for enhanced bias detection. Our dataset preparation follows the FAIR principles, ensuring ethical data usage. Through rigorous testing on various datasets, CBDT showcases its ability in distinguishing biased from neutral statements, while also pinpointing exact biased lexemes. Our approach outperforms existing methods, achieving a 2-4\% increase over benchmark performances. This opens avenues for adapting the CBDT model across diverse linguistic and cultural landscapes.