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

University Of Washington, Seattle WA

Investigators

Abstract

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 offer innovative and practical tools. However, this technology has inherent problems. Generative AI models learn implicit associations and cognitive errors documented in psychology from large-scale sociocultural data. Generative AI error pose implications for performance discrepancies 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 associations and errors that amplify issues that are challenging to mitigate for both AI developers and users. This project will develop methods for evaluating associations and errors 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 knowledge 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 student populations, 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. 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 errors in generative AI systems by developing principled and generalizable detection and measurement methods. Leveraging the findings and current evaluation methods, the research will devise approaches that automatically identify and reduce these 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 errors, 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 generative AI outputs will assess their influence on human 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 errors. 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.