ExplainabilityTransparencyTrustworthinesscs.AI
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction
Tom Bewley, Jonathan Lawry, Arthur Richards
January 17, 2022
arXiv.org
3 citations
Key Takeaways
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The paper develops a tool to make AI agent learning processes more understandable to humans by identifying and summarizing the most important moments of change during training, rather than requiring experts to analyze entire learning histories manually.
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The method works across different types of AI systems ("model-agnostic") and can handle complex, continuous environments, making it potentially applicable to various reinforcement learning agents used in real-world applications.
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The approach uses information theory to automatically detect what matters most, measuring differences in agent behavior over time and space to highlight pivotal learning moments—this data-driven approach reduces human bias in deciding what's important to examine.
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The technique produces both visual and textual summaries that can communicate agent behavior changes to non-technical stakeholders, addressing a key challenge in responsible AI: making opaque learning processes transparent and auditable.
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This complements rather than replaces existing interpretability methods, suggesting it's designed as part of a broader toolkit for understanding AI agents, which aligns with responsible AI practices that often require multiple verification approaches.
Abstract
We introduce a data-driven, model-agnostic technique for generating a human-interpretable summary of the salient points of contrast within an evolving dynamical system, such as the learning process of a control agent. It involves the aggregation of transition data along both spatial and temporal dimensions according to an information-theoretic divergence measure. A practical algorithm is outlined for continuous state spaces, and deployed to summarise the learning histories of deep reinforcement learning agents with the aid of graphical and textual communication methods. We expect our method to be complementary to existing techniques in the realm of agent interpretability.