Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models

• URL: http://arxiv.org/abs/2412.16355v1
• Date: Fri, 20 Dec 2024 21:34:43 GMT
• Title: Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models
• Authors: Adam Davies, Elisa Nguyen, Michael Simeone, Erik Johnston, Martin Gubri,
• Abstract summary: Social science has a long history of studying the social impacts of transformative technologies. We propose a conceptual framework studying foundation models as sociotechnical systems. We advocate for an interdisciplinary and collaborative research paradigm between AI and social science.
• Score: 1.24830234462377
• License:
• Abstract: With the rise of foundation models, there is growing concern about their potential social impacts. Social science has a long history of studying the social impacts of transformative technologies in terms of pre-existing systems of power and how these systems are disrupted or reinforced by new technologies. In this position paper, we build on prior work studying the social impacts of earlier technologies to propose a conceptual framework studying foundation models as sociotechnical systems, incorporating social science expertise to better understand how these models affect systems of power, anticipate the impacts of deploying these models in various applications, and study the effectiveness of technical interventions intended to mitigate social harms. We advocate for an interdisciplinary and collaborative research paradigm between AI and social science across all stages of foundation model research and development to promote socially responsible research practices and use cases, and outline several strategies to facilitate such research.

Related papers

• Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models [16.293574791587247]
  This paper proposes the "Intelligent Computing Social Modeling" (ICSM) method to address these issues. By simulating the U.S. presidential election, this study empirically demonstrates the operational pathways and methodological advantages of ICSM. The findings suggest that LLMs will drive methodological innovation in political science through integration and improvement rather than direct substitution.
  arXiv  Detail & Related papers  (2024-10-07T06:30:59Z)
• Affective Computing Has Changed: The Foundation Model Disruption [47.88090382507161]
  We aim to raise awareness of the power of Foundation Models in the field of Affective Computing. We synthetically generate and analyse multimodal affective data, focusing on vision, linguistics, and speech (acoustics) We discuss some fundamental problems, such as ethical issues and regulatory aspects, related to the use of Foundation Models in this research area.
  arXiv  Detail & Related papers  (2024-09-13T15:20:18Z)
• Decoding Digital Influence: The Role of Social Media Behavior in Scientific Stratification Through Logistic Attribution Method [6.285608271780605]
  This study comprehensively analyzes the impact of social media on scientific stratification and mobility. It uses an Explainable Logistic Analysis from a meso-level perspective to explore the correlation between social media behaviors and scientific social stratification.
  arXiv  Detail & Related papers  (2024-07-23T02:01:40Z)
• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
  We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans. In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
  arXiv  Detail & Related papers  (2023-10-18T02:27:01Z)
• Social Influence Dialogue Systems: A Scoping Survey of the Efforts Towards Influence Capabilities of Dialogue Systems [50.57882213439553]
  Social influence dialogue systems are capable of persuasion, negotiation, and therapy. There exists no formal definition or category for dialogue systems with these skills. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.
  arXiv  Detail & Related papers  (2022-10-11T17:57:23Z)
• Predicting Opinion Dynamics via Sociologically-Informed Neural Networks [31.77040611129394]
  We present Sociologically-Informed Neural Network (SINN), which integrates theoretical models and social media data. In particular, we recast theoretical models as ordinary differential equations (ODEs) We train a neural network that simultaneously approximates the data and conforms to the ODEs that represent the social scientific knowledge.
  arXiv  Detail & Related papers  (2022-07-07T05:55:47Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• On the Opportunities and Risks of Foundation Models [256.61956234436553]
  We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration.
  arXiv  Detail & Related papers  (2021-08-16T17:50:08Z)
• Simulating Social Acceptability With Agent-based Modeling [28.727916976371265]
  We suggest to reframe the social space as a dynamic bundle of social practices. We outline possible research directions that focus on specific interactions among practices as well as regularities in emerging patterns.
  arXiv  Detail & Related papers  (2021-05-14T09:31:43Z)
• A Simulation Model Demonstrating the Impact of Social Aspects on Social Internet of Things [0.0]
  This paper studies the impact of social behavior on the interaction pattern of social objects. The model proposed in this paper studies the implications of competitive vs. cooperative social paradigm. It is proved that cooperative strategy is more efficient than competitive strategy.
  arXiv  Detail & Related papers  (2020-02-23T07:18:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.