arXiv:2410.23724v1 Announce Type: new
Abstract: This chapter provides an overview of research works that present approaches with some degree of cross-fertilisation between Computational Argumentation and Machine Learning. Our review of the literature identified two broad themes representing the purpose of the interaction between these two areas: argumentation for machine learning and machine learning for argumentation. Across these two themes, we systematically evaluate the spectrum of works across various dimensions, including the type of learning and the form of argumentation framework used. Further, we identify three types of interaction between these two areas: synergistic approaches, where the Argumentation and Machine Learning components are tightly integrated; segmented approaches, where the two are interleaved such that the outputs of one are the inputs of the other; and approximated approaches, where one component shadows the other at a chosen level of detail. We draw conclusions about the suitability of certain forms of Argumentation for supporting certain types of Machine Learning, and vice versa, with clear patterns emerging from the review. Whilst the reviewed works provide inspiration for successfully combining the two fields of research, we also identify and discuss limitations and challenges that ought to be addressed in order to ensure that they remain a fruitful pairing as AI advances.
Cross-Fertilisation between Computational Argumentation and Machine Learning
This chapter provides a comprehensive overview of research works that explore the interconnections between Computational Argumentation and Machine Learning. Both fields are highly multi-disciplinary, drawing from various branches of computer science, philosophy, and cognitive science, among others. By examining the literature, the authors identify two overarching themes: argumentation for machine learning and machine learning for argumentation.
Argumentation for Machine Learning
The first theme, argumentation for machine learning, focuses on how the principles and techniques of computational argumentation can improve machine learning systems. This includes using argumentation frameworks to explain the decisions made by machine learning algorithms and provide interpretability to their outputs. By generating arguments and counterarguments, the transparency and trustworthiness of machine learning systems can be enhanced, which is crucial for domains where explainability is required, such as healthcare or legal settings. Additionally, argumentation can help identify biases and inconsistencies in the training data or model, leading to fairer and more robust machine learning systems.
Machine Learning for Argumentation
The second theme, machine learning for argumentation, explores how machine learning techniques can be leveraged to complement and enhance computational argumentation frameworks. This involves using machine learning algorithms to analyze large-scale argumentation corpora and extract patterns, trends, and semantic relationships. By automatically classifying arguments, identifying fallacies, or predicting the outcomes of argumentation processes, machine learning can speed up and improve argumentation-based systems. Furthermore, machine learning can assist in the automatic generation of arguments, helping users construct persuasive arguments or counterarguments in support of their claims.
Spectrum of Works and Types of Interaction
Within these two themes, the authors systematically evaluate a spectrum of works across various dimensions. They consider the type of learning employed, such as supervised learning, reinforcement learning, or unsupervised learning, as well as the form of argumentation framework used, such as formal logic-based frameworks or probabilistic graphical models. By analyzing these dimensions, the authors gain insights into the suitability of different forms of argumentation for supporting specific types of machine learning, and vice versa.
Furthermore, the authors identify and categorize three types of interaction between argumentation and machine learning:
- Synergistic Approaches: In these approaches, argumentation and machine learning components are tightly integrated, forming an inseparable whole. This integration allows for bidirectional interactions, where the argumentation framework informs the learning process, and learned models influence the argumentation process. Synergistic approaches exhibit a high level of synergy between the two fields and often result in more valuable and robust systems.
- Segmented Approaches: Here, the argumentation and machine learning components are interleaved, with the outputs of one becoming the inputs of the other. This interleaving allows for iterative refinement and improvement of both argumentation and learning. By continuously exchanging information and feedback, segmented approaches can achieve better performance and adaptability.
- Approximated Approaches: These approaches involve one component shadowing the other at a chosen level of detail. For instance, a complex argumentation framework might be approximated by a simpler model that captures essential aspects. This approximation allows for scalability and efficiency in processing large-scale argumentation datasets, while still preserving the integrity and reliability of the argumentation process.
Conclusion and Future Directions
This review of the literature provides valuable insights into the integration of computational argumentation and machine learning. It highlights the benefits and potentials of combining these two fields, showcasing successful examples of cross-fertilisation. However, it also acknowledges limitations and challenges that need to be addressed to ensure the continued success of this interdisciplinary synergy as AI advances.
The multi-disciplinary nature of the concepts discussed in the content demonstrates the interconnectedness of computer science, philosophy, cognitive science, and other relevant disciplines. This interconnectedness emphasizes the importance of collaboration and knowledge-sharing across these domains to drive advancements in both computational argumentation and machine learning.
As future directions, researchers could explore more advanced forms of synergistic approaches, finding new ways to tightly integrate argumentation and machine learning methods. Additionally, the development of hybrid models combining formal argumentation frameworks with deep learning techniques could further enhance the interpretability and performance of machine learning systems.
Moreover, addressing challenges such as the scalability of argumentation frameworks, handling uncertain or incomplete data, and developing robust methods for bias detection and mitigation will be critical for the continued progress of this interplay between argumentation and machine learning.
Overall, this review provides a solid foundation for researchers and practitioners to understand the current state of cross-fertilisation between computational argumentation and machine learning and inspires future work towards advancing these fields together in a harmonious manner.