Maud van Lier presents a methodological framework for understanding artificial agency in the context of basic research, particularly in AI-driven science. The Four-Fold Framework, as the author coins it, is a pluralistic and pragmatic approach that incorporates Gricean modeling, analogical modeling, theoretical modeling, and conceptual modeling. The motivation behind this framework lies in the increasingly active role that AI systems are taking on in scientific research, warranting the development of a robust conceptual foundation for these ‘agents.’
The author critically assesses Sarkia’s neo-Gricean framework, which offers three modeling strategies for conceptualizing artificial agency. While acknowledging its merits, the author identifies a crucial shortcoming in its lack of a semantic dimension, which is necessary to bridge the gap between theoretical models and practical implementation in basic research. To address this issue, the author proposes the addition of conceptual modeling as a fourth strategy, ultimately forming the Four-Fold Framework. This new framework aims to provide a comprehensive account of artificial agency in basic research by accommodating different interpretations and addressing the semantic dimension of artificial agency.
By implementing the Four-Fold Framework, the author posits that researchers will be able to develop a more inclusive and pragmatically plausible understanding of artificial agency in the context of AI-driven science. The framework sets the stage for a robust conceptual foundation that can accommodate the complexities and nuances of artificial agency as AI continues to evolve and expand its role in scientific research.
This paper’s exploration of artificial agency also contributes to the broader philosophical discourse on agency and autonomy in the context of artificial intelligence. As AI systems become more advanced, the distinction between human and artificial agents blurs, raising questions about the nature of agency, responsibility, and ethical considerations. The Four-Fold Framework provides a methodological tool to examine these complex issues, grounding the analysis of artificial agency within a rigorous and comprehensive structure.
Future research can expand upon the Four-Fold Framework by investigating its applicability to other emerging areas in AI, such as AI ethics, human-AI collaboration, and autonomous decision-making. Additionally, researchers can explore how the Four-Fold Framework might inform the development of AI-driven science policy and governance, ensuring that ethical, legal, and societal implications are considered in the integration of artificial agency in scientific research. By refining and extending the Four-Fold Framework, the academic community can better anticipate and navigate the challenges and opportunities that artificial agency presents in the rapidly evolving landscape of AI-driven science.
Abstract
Recent developments in AI-research suggest that an AI-driven science might not be that far off. The research of for Melnikov et al. (2018) and that of Evans et al. (2018) show that automated systems can already have a distinctive role in the design of experiments and in directing future research. Common practice in many of the papers devoted to the automation of basic research is to refer to these automated systems as ‘agents’. What is this attribution of agency based on and to what extent is this an important notion in the broader context of an AI-driven science? In an attempt to answer these questions, this paper proposes a new methodological framework, introduced as the Four-Fold Framework, that can be used to conceptualize artificial agency in basic research. It consists of four modeling strategies, three of which were already identified and used by Sarkia (2021) to conceptualize ‘intentional agency’. The novelty of the framework is the inclusion of a fourth strategy, introduced as conceptual modeling, that adds a semantic dimension to the overall conceptualization. The strategy connects to the other strategies by modeling both the actual use of ‘artificial agency’ in basic research as well as what is meant by it in each of the other three strategies. This enables researchers to bridge the gap between theory and practice by comparing the meaning of artificial agency in both an academic as well as in a practical context.
Introducing a four-fold way to conceptualize artificial agency