Towards Unified Attribution in Explainable AI, Data-Centric AI, and Mechanistic Interpretability (2501.18887v3)

Abstract: The increasing complexity of AI systems has made understanding their behavior critical. Numerous interpretability methods have been developed to attribute model behavior to three key aspects: input features, training data, and internal model components, which emerged from explainable AI, data-centric AI, and mechanistic interpretability, respectively. However, these attribution methods are studied and applied rather independently, resulting in a fragmented landscape of methods and terminology. This position paper argues that feature, data, and component attribution methods share fundamental similarities, and a unified view of them benefits both interpretability and broader AI research. To this end, we first analyze popular methods for these three types of attributions and present a unified view demonstrating that these seemingly distinct methods employ similar techniques (such as perturbations, gradients, and linear approximations) over different aspects and thus differ primarily in their perspectives rather than techniques. Then, we demonstrate how this unified view enhances understanding of existing attribution methods, highlights shared concepts and evaluation criteria among these methods, and leads to new research directions both in interpretability research, by addressing common challenges and facilitating cross-attribution innovation, and in AI more broadly, with applications in model editing, steering, and regulation.

• The paper introduces a unified framework combining feature, data, and component attributions to systematically interpret AI behavior.
• It leverages techniques like perturbation, gradients, and linear approximations to measure element contributions toward model predictions.
• The framework paves the way for improved model transparency, robustness, and policy guidance in high-stakes AI applications.

Advancing Interpretability by Unifying Attribution Methods in AI

The paper "Building Bridges, Not Walls - Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution" posits a novel framework aimed at understanding the intricate behavior of complex AI systems. As AI models have advanced in complexity, the ability to interpret these models has become increasingly challenging. The authors address this by unifying attribution methods that apply across three core dimensions: feature, data, and internal model components attribution.

Unified Framework for Attribution Methods

The researchers argue that despite the traditional fragmentation observed in the domains of feature, data, and component attributions, these methodologies can be connected through shared techniques such as perturbations, gradients, and linear approximations. They offer a unified perspective that groups these seemingly disparate methods under a cohesive framework, emphasizing a coherent methodological foundation rather than focusing on their individual application domains.

In section \ref{sec:problem}, the authors introduce the unified attribution problem framework that aligns feature, data, and component attributions. Their methodology involves systematically representing the model's behavior concerning input features, training data, and internal components through attribution functions that quantify the contribution of these individual elements toward a model's prediction. For instance, gradients indicate how model outputs are influenced by features, while perturbations assess the effect when entire training instances are removed.

Deep Dive into Attribution Types

The document discusses each type of attribution in detail:

1. Feature Attribution: This is explored through methods that leverage various perturbation-based approaches (such as Shapley values and Occlusion), gradient-based techniques (e.g., Integrated Gradients), and linear approximation strategies (e.g., LIME).
2. Data Attribution: This area utilizes methods like the Leave-One-Out (LOO) process and influence functions to assess the impact of specific training instances on model performance. The paper highlights the potential of these methods to uncover influential outliers or biased training samples efficiently.
3. Component Attribution: While relatively newer, this involves the reverse engineering of complex models to decipher which internal components, like neurons or layers, are influential for specific tasks. Techniques aligning with causal mediation analysis dominate this space.

Commonalities and Challenges

One of the paper's strengths lies in highlighting the common challenges across attribution methods — computational complexity, variability in technique outcomes, and the difficulty in evaluating and granting consistency in attributions. These commonalities make it apparent that despite their varied focus, feature, data, and component attributions share foundational hurdles that can benefit from unified investigative approaches.

Implications for Future AI Research

Through its unified framework, this work not only impacts interpretability but also opens avenues for broader AI applications, like model editing and regulation. The ability to comprehensively understand a model's behavior could lead to more robust, transparent AI systems that align with regulatory requirements and ethical norms.

Future Directions: The unification theme is expected to inspire future research to explore deeper interactions and relations across attribution types. Developing tools that leverage cross-domain knowledge can potentially address existing challenges and create a more cohesive understanding of AI systems.

Concluding Thoughts: Overall, the paper adeptly positions itself as a pivotal work advocating for a holistic view of interpretability tools in AI. This unified perspective is well-timed in the continual quest for transparent and interpretable machine learning models in increasingly high-stakes applications. The framework demonstrates promise not only in advancing the technical understanding but also in guiding policy and ethical standards surrounding AI applications.