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16737 latest Fairness/Ethics + ML/AI papers

A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work

Brian E. Perron, Kelley A. Rivenburgh, Bryan G. Victor, Zia Qi, Hui Luan

arXiv:2411.07156v1 »Full PDF »

37 pages, 3 figures

Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representations capture meaning and relationships in text data more effectively than traditional keyword-based approaches. We discuss fundamental concepts, technical foundations, and practical applications, including semantic search, clustering, and retrieval augmented generation. The paper demonstrates how embeddings can enhance research workflows through concrete examples from social work practice, such as analyzing case notes for housing instability patterns and comparing social work licensing examinations across languages. While highlighting the potential of embeddings for advancing social work research, we acknowledge limitations including information loss, training data constraints, and potential biases. We conclude that successfully implementing embedding technologies in social work requires developing domain-specific models, creating accessible tools, and establishing best practices aligned with social work's ethical principles. This integration can enhance our ability to analyze complex patterns in text data while supporting more effective services and interventions.Abstract

MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue

Fengxiang Wang, Ranjie Duan, Peng Xiao, Xiaojun Jia, YueFeng Chen, Chongwen Wang, Jialing Tao, Hang Su, Jun Zhu, Hui Xue

arXiv:2411.03814v1 »Full PDF »
Large Language Models (LLMs) demonstrate outstanding performance in their reservoir of knowledge and understanding capabilities, but they have also been shown to be prone to illegal or unethical reactions when subjected to jailbreak attacks. To ensure their responsible deployment in critical applications, it is crucial to understand the safety capabilities and vulnerabilities of LLMs. Previous works mainly focus on jailbreak in single-round dialogue, overlooking the potential jailbreak risks in multi-round dialogues, which are a vital way humans interact with and extract information from LLMs. Some studies have increasingly concentrated on the risks associated with jailbreak in multi-round dialogues. These efforts typically involve the use of manually crafted templates or prompt engineering techniques. However, due to the inherent complexity of multi-round dialogues, their jailbreak performance is limited. To solve this problem, we propose a novel multi-round dialogue jailbreaking agent, emphasizing the importance of stealthiness in identifying and mitigating potential threats to human values posed by LLMs. We propose a risk decomposition strategy that distributes risks across multiple rounds of queries and utilizes psychological strategies to enhance attack strength. Extensive experiments show that our proposed method surpasses other attack methods and achieves state-of-the-art attack success rate. We will make the corresponding code and dataset available for future research. The code will be released soon.Abstract

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4

Hui Huang, Yingqi Qu, Xingyuan Bu, Hongli Zhou, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao

arXiv:2403.02839v3 »Full PDF »
Recently, there has been a growing trend of utilizing Large Language Model (LLM) to evaluate the quality of other LLMs. Many studies have employed proprietary close-sourced models, especially GPT-4, as the evaluator. Alternatively, other works have fine-tuned judge models based on open-source LLMs as the evaluator. While the fine-tuned judge models are claimed to achieve comparable evaluation capability with GPT-4, in this work, we conduct an empirical study of judge models. Our findings indicate that although the fine-tuned judge models achieve high performance on in-domain test sets, even surpassing GPT-4, they underperform GPT-4 across several dimensions, including generalizability, fairness, aspect-specific evaluation, and scalability. We also reveal that the fine-tuned judge model inherently operates as a task-specific classifier, consequently imposing the limitations. Finally, we introduce a integrated method, leveraging GPT-4 to compensate for the limitations and improve the fine-tuned judges. Experiment results show our method achieves accuracy on par with GPT-4 with only 50% of the API expense.Abstract

Building Altruistic and Moral AI Agent with Brain-inspired Affective Empathy Mechanisms

Feifei Zhao, Hui Feng, Haibo Tong, Zhengqiang Han, Enmeng Lu, Yinqian Sun, Yi Zeng

arXiv:2410.21882v1 »Full PDF »
As AI closely interacts with human society, it is crucial to ensure that its decision-making is safe, altruistic, and aligned with human ethical and moral values. However, existing research on embedding ethical and moral considerations into AI remains insufficient, and previous external constraints based on principles and rules are inadequate to provide AI with long-term stability and generalization capabilities. In contrast, the intrinsic altruistic motivation based on empathy is more willing, spontaneous, and robust. Therefore, this paper is dedicated to autonomously driving intelligent agents to acquire morally behaviors through human-like affective empathy mechanisms. We draw inspiration from the neural mechanism of human brain's moral intuitive decision-making, and simulate the mirror neuron system to construct a brain-inspired affective empathy-driven altruistic decision-making model. Here, empathy directly impacts dopamine release to form intrinsic altruistic motivation. Based on the principle of moral utilitarianism, we design the moral reward function that integrates intrinsic empathy and extrinsic self-task goals. A comprehensive experimental scenario incorporating empathetic processes, personal objectives, and altruistic goals is developed. The proposed model enables the agent to make consistent moral decisions (prioritizing altruism) by balancing self-interest with the well-being of others. We further introduce inhibitory neurons to regulate different levels of empathy and verify the positive correlation between empathy levels and altruistic preferences, yielding conclusions consistent with findings from psychological behavioral experiments. This work provides a feasible solution for the development of ethical AI by leveraging the intrinsic human-like empathy mechanisms, and contributes to the harmonious coexistence between humans and AI.Abstract

Is Your HD Map Constructor Reliable under Sensor Corruptions?

Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, Hui Zhang, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, Jing Zhang

arXiv:2406.12214v3 »Full PDF »

NeurIPS 2024; 40 pages, 17 figures, 23 tables; Code at https://mapbench.github.io/

Driving systems often rely on high-definition (HD) maps for precise environmental information, which is crucial for planning and navigation. While current HD map constructors perform well under ideal conditions, their resilience to real-world challenges, \eg, adverse weather and sensor failures, is not well understood, raising safety concerns. This work introduces MapBench, the first comprehensive benchmark designed to evaluate the robustness of HD map construction methods against various sensor corruptions. Our benchmark encompasses a total of 29 types of corruptions that occur from cameras and LiDAR sensors. Extensive evaluations across 31 HD map constructors reveal significant performance degradation of existing methods under adverse weather conditions and sensor failures, underscoring critical safety concerns. We identify effective strategies for enhancing robustness, including innovative approaches that leverage multi-modal fusion, advanced data augmentation, and architectural techniques. These insights provide a pathway for developing more reliable HD map construction methods, which are essential for the advancement of autonomous driving technology. The benchmark toolkit and affiliated code and model checkpoints have been made publicly accessible.Abstract

A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement

Hui Yuan, Yifan Zeng, Yue Wu, Huazheng Wang, Mengdi Wang, Liu Leqi

arXiv:2410.13828v1 »Full PDF »
Reinforcement Learning from Human Feedback (RLHF) has become the predominant approach for language model (LM) alignment. At its core, RLHF uses a margin-based loss for preference optimization, specifying ideal LM behavior only by the difference between preferred and dispreferred responses. In this paper, we identify a common pitfall of margin-based methods -- the under-specification of ideal LM behavior on preferred and dispreferred responses individually, which leads to two unintended consequences as the margin increases: (1) The probability of dispreferred (e.g., unsafe) responses may increase, resulting in potential safety alignment failures. (2) The probability of preferred responses may decrease, even when those responses are ideal. We demystify the reasons behind these problematic behaviors: margin-based losses couple the change in the preferred probability to the gradient of the dispreferred one, and vice versa, often preventing the preferred probability from increasing while the dispreferred one decreases, and thus causing a synchronized increase or decrease in both probabilities. We term this effect, inherent in margin-based objectives, gradient entanglement. Formally, we derive conditions for general margin-based alignment objectives under which gradient entanglement becomes concerning: the inner product of the gradients of preferred and dispreferred log-probabilities is large relative to the individual gradient norms. We theoretically investigate why such inner products can be large when aligning language models and empirically validate our findings. Empirical implications of our framework extend to explaining important differences in the training dynamics of various preference optimization algorithms, and suggesting potential algorithm designs to mitigate the under-specification issue of margin-based methods and thereby improving language model alignment.Abstract

Weak-to-Strong Generalization beyond Accuracy: a Pilot Study in Safety, Toxicity, and Legal Reasoning

Ruimeng Ye, Yang Xiao, Bo Hui

arXiv:2410.12621v1 »Full PDF »
As large language models (LLMs) continue to advance, ensuring their alignment with human values becomes increasingly critical. Traditional alignment methods heavily rely on human feedback to fine-tune models. With the emergence of superhuman models whose outputs may surpass human understanding, evaluating and aligning these models using human judgments poses significant challenges. To address the challenges, recent works use weak supervisors to elicit knowledge from much stronger models. However, there are important disanalogies between the empirical setup in the existing works and the genuine goal of alignment. We remark that existing works investigate the phenomenon of weak-to-strong generation in analogous setup (i.e., binary classification), rather than practical alignment-relevant tasks (e.g., safety). In this paper, we bridge this gap by extending weak-to-strong generation to the context of practical alignment. We empirically demonstrate the widespread phenomenon of weak-to-strong generation in three complicated alignment tasks: safety, toxicity, and legal reasoning}. Furthermore, we explore efficient strategies for improving alignment performance to enhance the quality of model outcomes. Lastly, we summarize and analyze the challenges and potential solutions in regard to specific alignment tasks, which we hope to catalyze the research progress on the topic of weak-to-strong generalization. Our code is released at https://github.com/yeruimeng/WTS.git.Abstract

Gender Bias of LLM in Economics: An Existentialism Perspective

Hui Zhong, Songsheng Chen, Mian Liang

arXiv:2410.19775v1 »Full PDF »

Gender Bias, Large Language Models, Decision-Making

Large Language Models (LLMs), such as GPT-4 and BERT, have rapidly gained traction in natural language processing (NLP) and are now integral to financial decision-making. However, their deployment introduces critical challenges, particularly in perpetuating gender biases that can distort decision-making outcomes in high-stakes economic environments. This paper investigates gender bias in LLMs through both mathematical proofs and empirical experiments using the Word Embedding Association Test (WEAT), demonstrating that LLMs inherently reinforce gender stereotypes even without explicit gender markers. By comparing the decision-making processes of humans and LLMs, we reveal fundamental differences: while humans can override biases through ethical reasoning and individualized understanding, LLMs maintain bias as a rational outcome of their mathematical optimization on biased data. Our analysis proves that bias in LLMs is not an unintended flaw but a systematic result of their rational processing, which tends to preserve and amplify existing societal biases encoded in training data. Drawing on existentialist theory, we argue that LLM-generated bias reflects entrenched societal structures and highlights the limitations of purely technical debiasing methods. This research underscores the need for new theoretical frameworks and interdisciplinary methodologies that address the ethical implications of integrating LLMs into economic and financial decision-making. We advocate for a reconceptualization of how LLMs influence economic decisions, emphasizing the importance of incorporating human-like ethical considerations into AI governance to ensure fairness and equity in AI-driven financial systems.Abstract

Learning Fair Models without Sensitive Attributes: A Generative Approach

Huaisheng Zhu, Enyan Dai, Hui Liu, Suhang Wang

arXiv:2203.16413v2 »Full PDF »
Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues. The lack of sensitive attributes challenges many existing fair classifiers. Though we lack sensitive attributes, for many applications, there usually exists features or information of various formats that are relevant to sensitive attributes. For example, purchase history of a person can reflect his or her race, which would help for learning fair classifiers on race. However, the work on exploring relevant features for learning fair models without sensitive attributes is rather limited. Therefore, in this paper, we study a novel problem of learning fair models without sensitive attributes by exploring relevant features. We propose a probabilistic generative framework to effectively estimate the sensitive attribute from the training data with relevant features in various formats and utilize the estimated sensitive attribute information to learn fair models. Experimental results on real-world datasets show the effectiveness of our framework in terms of both accuracy and fairness.Abstract

Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting

Zhuoning Guo, Hao Liu, Le Zhang, Qi Zhang, Hengshu Zhu, Hui Xiong

arXiv:2409.19545v1 »Full PDF »
Labor market forecasting on talent demand and supply is essential for business management and economic development. With accurate and timely forecasts, employers can adapt their recruitment strategies to align with the evolving labor market, and employees can have proactive career path planning according to future demand and supply. However, previous studies ignore the interconnection between demand-supply sequences among different companies and positions for predicting variations. Moreover, companies are reluctant to share their private human resource data for global labor market analysis due to concerns over jeopardizing competitive advantage, security threats, and potential ethical or legal violations. To this end, in this paper, we formulate the Federated Labor Market Forecasting (FedLMF) problem and propose a Meta-personalized Convergence-aware Clustered Federated Learning (MPCAC-FL) framework to provide accurate and timely collaborative talent demand and supply prediction in a privacy-preserving way. First, we design a graph-based sequential model to capture the inherent correlation between demand and supply sequences and company-position pairs. Second, we adopt meta-learning techniques to learn effective initial model parameters that can be shared across companies, allowing personalized models to be optimized for forecasting company-specific demand and supply, even when companies have heterogeneous data. Third, we devise a Convergence-aware Clustering algorithm to dynamically divide companies into groups according to model similarity and apply federated aggregation in each group. The heterogeneity can be alleviated for more stable convergence and better performance. Extensive experiments demonstrate that MPCAC-FL outperforms compared baselines on three real-world datasets and achieves over 97% of the state-of-the-art model, i.e., DH-GEM, without exposing private company data.Abstract