CAREER: The Impact of Associations and Biases in Generative AI on Society

该项目旨在巩固生成式人工智能（AI）的伦理基础。生成式人工智能系统建立在语言、语音和视觉机器学习模型的单模态和多模态组合上。生成式人工智能模型，如聊天机器人ChatGPT和文本到图像生成器Stable Diffusion提供了创新和实用的工具。然而，这项技术有其固有的问题。生成式人工智能模型从大规模的社会文化数据中学习认知心理学中记录的内隐关联和偏见，这是人类关于性别、种族或民族、社会阶层、年龄、能力、性、国籍、宗教、概念和交叉关联的偏见的来源。生成式人工智能偏见对人工智能的表现差异以及人工智能伦理产生了影响，特别是关于生成式人工智能对个人和社会的影响。易于访问的生成式人工智能模型的输出包含有偏见的社会关联，放大了复杂的偏见，这对人工智能开发人员和用户来说都是具有挑战性的。该项目将开发评估生成人工智能中的关联和偏见的方法，评估生成人工智能对社会的影响，并分析生成人工智能如何塑造人类的认知和代理。该项目将通过开发解决机器、人类与人工智能合作和社会中的偏见的方法来促进公民权利。该奖项提供的开源工具和材料将提高一系列利益相关者的意识，包括不同的学生群体、研究人员、开发人员、行业、开源社区、人工智能用户、政策制定者和公众。这一努力将通过引入跨学科和部门的生成式人工智能伦理课程，加强华盛顿大学的人工智能教育。随着人工智能法规和立法的制定，该奖项产生的科学证据将为决策者提供有关安全、可靠和值得信赖的人工智能开发和使用的信息。与政策智库的积极合作将有助于将知识转移到政策制定中。该项目将把机器学习、自然语言处理、计算机视觉、语音处理、人机交互等方面的计算机和信息科学研究与社会认知的方法和大规模数据集相结合。该项目的主要目标是实证分析生成式人工智能的社会影响，为人工智能的道德和负责任的开发和部署做出贡献。该项目旨在通过开发原则性和可推广的检测和测量方法，评估和表征生成式人工智能系统中的关联和偏见。利用这些发现和开发的偏见评估方法，该研究将设计出自动识别和减少生成人工智能模型中的偏见信号的方法，同时考虑到特定的任务、应用、上下文和用例。这些方法将包括训练数据增强、嵌入空间处理、微调、指令调整和从反馈中强化学习等技术。通过将生成式人工智能的偏见与州和国家层面上人类的隐性和显性偏见进行比较，并识别新兴的生成式人工智能偏见，该项目将揭示生成式人工智能更广泛的社会影响。在接触有偏见的生成人工智能输出后，对人类内隐联想测试分数和决策的变化进行分析，将评估它们对人类偏见、感知和人工智能协作决策的影响。因此，该奖项将开发新的方法，通过引入新的关联来减轻生成人工智能偏见造成的负面后果，使生成人工智能与人类价值观保持一致。这个奖项带来的潜在进步超越了计算机和信息科学，为认知科学、心理学、语言学、社会学和政治学提供了工具和见解，同时为哲学、法律和政策等领域提供了信息。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to solidify the foundations of ethics in generative artificial intelligence (AI). Generative AI systems are built on unimodal and multimodal combinations of language, speech, and vision machine learning models. Generative AI models such as the chatbot ChatGPT and the text-to-image generator Stable Diffusion offer innovative and practical tools. However, this technology has inherent problems. Generative AI models learn implicit associations and biases documented in cognitive psychology from large-scale sociocultural data, which is a source of human biases regarding gender, race or ethnicity, social class, age, ability, sexuality, nationality, religion, concepts, and intersectional associations. Generative AI bias poses implications for performance disparities in AI, as well as AI ethics, particularly concerning the impact of generative AI on individuals and society. Outputs from easily accessible generative AI models contain biased social associations, amplifying complex biases that are challenging to mitigate for both AI developers and users. This project will develop methods for evaluating associations and biases in generative AI, assess the impact of generative AI on society, and analyze how generative AI shapes human cognition and agency. The project will advance civil rights by developing methods to address bias in machines, human-AI collaboration, and society. The open-source tools and materials presented by this award will raise awareness among a range of stakeholders, including the diverse student population, researchers, developers, industry, the open-source community, AI users, policymakers, and the public. This effort will enhance AI education at the University of Washington by introducing a generative AI ethics curriculum across disciplines and divisions. As AI regulation and legislation are being formulated, the scientific evidence produced by this award will inform policymakers on the safe, secure, and trustworthy development and use of AI. Active collaboration with policy think-tanks will aid transfer of the knowledge to policymaking.This project will integrate computer and information science research in machine learning, natural language processing, computer vision, speech processing, and human-AI interaction with methodologies and large-scale datasets from social cognition. The project's primary objective is to empirically analyze the societal impact of generative AI, contributing to the ethical and responsible development and deployment of AI. The project seeks to evaluate and characterize associations and biases in generative AI systems by developing principled and generalizable detection and measurement methods. Leveraging the findings and developed bias evaluation methods, the research will devise approaches that automatically identify and reduce bias signals in generative AI models, taking into account the specific task, application, context, and use case. These approaches will encompass techniques such as training data augmentation, embedding space processing, fine-tuning, instruction tuning, and reinforcement learning from feedback. By examining generative AI biases in comparison to implicit and explicit biases of humans at the state and country levels and identifying emergent generative AI biases, the project will uncover the broader societal impact of generative AI. The analysis of changes in human implicit association test scores and decisions following exposure to biased generative AI outputs will assess their influence on human biases, perception, and decisions in human-AI collaboration. Accordingly, the award will develop novel approaches that align generative AI with human values by introducing new associations to mitigate the negative consequences caused by generative AI biases. The potential advancements resulting from this award extend beyond computer and information science, providing tools and insights for cognitive science, psychology, linguistics, sociology, and political science, while informing fields such as philosophy, law, and policy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.