arXiv:2411.16709v1 Announce Type: new
Abstract: In this report, I provide a brief summary of the literature in philosophy, psychology and cognitive science about Explanatory Virtues, and link these concepts to eXplainable AI.
Explanatory Virtues: A Multidisciplinary Perspective
Explanations are fundamental for understanding the world around us and making informed decisions. They provide us with insights into cause-and-effect relationships, underlying mechanisms, and the reasoning behind complex phenomena. However, not all explanations are created equal. The concept of explanatory virtues, explored in the fields of philosophy, psychology, and cognitive science, sheds light on the qualities that make explanations particularly valuable.
The Philosophical Perspective
In philosophy, the notion of explanatory virtues has been extensively discussed, with philosophers examining what constitutes a good explanation. Traditionally, explanatory virtues include simplicity, coherence, testability, and scope. A good explanation is often characterized by its simplicity, providing a concise account of the phenomenon at hand. Coherence refers to the explanation’s ability to align with existing knowledge and theories, creating a coherent framework. Testability emphasizes the importance of empirical evidence and the potential for verification. Lastly, scope indicates the breadth of the explanatory power, encompassing a wide range of phenomena.
The Psychological Perspective
Psychologists have delved into the cognitive processes underlying explanations and identified additional explanatory virtues. One such virtue is transparency, which relates to the accessibility and clarity of an explanation. A transparent explanation enables understanding by breaking down complex concepts into simpler, more digestible parts. Furthermore, psychologists have highlighted the importance of causal consistency, coherence with prior beliefs, and inferential robustness. These virtues ensure that explanations align with our mental models, internal consistency, and inferential stability, respectively.
The Link to eXplainable AI
eXplainable AI (XAI) is an emerging area of research focused on developing AI algorithms and systems that can provide interpretable and understandable explanations for their outputs. Understanding the multidisciplinary nature of explanatory virtues is crucial for the advancement of XAI.
From a philosophical perspective, XAI should strive to embody the traditional explanatory virtues of simplicity, coherence, testability, and scope. AI systems should aim to provide concise, coherent, empirically verifiable, and comprehensive explanations that stand up to scrutiny.
Psychological insights suggest that XAI systems should prioritize transparency, ensuring that explanations are accessible and comprehensible to end-users. Additionally, the principles of causal consistency, coherence with prior beliefs, and inferential robustness should be considered to enhance the trustworthiness and usability of AI explanations.
By combining the philosophically derived explanatory virtues with the psychological understanding of human cognition, XAI can create explanations that not only fulfill the requirements of AI systems but also meet the cognitive needs and expectations of human users.
Future Directions
The integration of explanatory virtues into XAI is an ongoing and interdisciplinary endeavor, and future research should continue to explore the multi-faceted nature of explanations. Cross-pollination between philosophy, psychology, and cognitive science, along with AI research, can advance our understanding of what makes explanations meaningful and valuable.
Further investigations can focus on identifying additional explanatory virtues specific to AI systems, taking into account factors like fairness, bias, and accountability. Additionally, interdisciplinary collaborations can help tailor explanations to different user groups, accounting for variations in cognitive abilities, expertise, and cultural backgrounds.
As XAI continues to evolve, the insights gained from philosophy, psychology, and cognitive science regarding explanatory virtues will play a crucial role in ensuring the development of AI systems that are not only explainable but also trustworthy, transparent, and agreeable to human users.