A Multimodal Automated Interpretability Agent (2404.14394v2)

Abstract: This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. It equips a pre-trained vision-LLM with a set of tools that support iterative experimentation on subcomponents of other models to explain their behavior. These include tools commonly used by human interpretability researchers: for synthesizing and editing inputs, computing maximally activating exemplars from real-world datasets, and summarizing and describing experimental results. Interpretability experiments proposed by MAIA compose these tools to describe and explain system behavior. We evaluate applications of MAIA to computer vision models. We first characterize MAIA's ability to describe (neuron-level) features in learned representations of images. Across several trained models and a novel dataset of synthetic vision neurons with paired ground-truth descriptions, MAIA produces descriptions comparable to those generated by expert human experimenters. We then show that MAIA can aid in two additional interpretability tasks: reducing sensitivity to spurious features, and automatically identifying inputs likely to be mis-classified.

• The paper introduces MAIA, an automated system that leverages vision-language models and diverse APIs to replicate human interpretability experiments.
• The paper demonstrates MAIA’s effectiveness in describing neuron behaviors and identifying modifications to reduce spurious feature sensitivity.
• The paper highlights MAIA's potential to enhance model transparency and support regulatory compliance through robust, automated experimentations.

Multimodal Automated Interpretability Agent (MAIA): Automating Neural Model Understanding

Overview of the MAIA System

The paper introduces the Multimodal Automated Interpretability Agent (MAIA), an automated system designed to facilitate the interpretation of neural models by automating tasks traditionally carried out by human researchers. MAIA instrumentalizes a vision-LLM integrated with an API consisting of various tools for conducting experimental probes into neural systems. This includes tasks such as synthesizing and editing inputs, computing exemplars that maximally activate network units, and summarizing experimental outcomes. The system's goal is to blend the dexterity of human experimental exploration with the scalable capabilities of automated processes, by composing and executing interpretability experiments in response to user-defined queries about system behavior.

Functional Capabilities and Evaluation

Neural Model Interpretation Tasks

MAIA's framework demonstrated effectiveness across several neural model interpretation tasks:

• Description of Neuron Behaviors: Using a novel dataset of synthetic neurons and multiple real-world trained models, MAIA generated neuron descriptions that matched or surpassed the quality of baseline methods and, in many cases, were comparable to those produced by expert human experimenters.
• Identification and Modification of Spurious Features: MAIA could successfully identify and suggest modifications to reduce model sensitivity to spurious image features, proving useful in enhancing model robustness against distribution shifts.
• Bias Detection in Image Classification: When applied to a standard image classification model, MAIA could automatically surface biases, indicating potential areas where model performance might degrade due to uneven dataset representations.

Predictive Performance

MAIA's descriptions of neuron functions were quantitatively evaluated against human-generated descriptions and existing automated methods. In comparative tests, MAIA's descriptions led to image activations that closely matched expectations set by ground truth selectivities, particularly in synthetic neuron settings. This points to MAIA's potential for reliable automated extractive description writing, which earlier systems struggled with, primarily due to low precision and the absence of direct hypothesis testing in their methodologies.

Implications for Future AI Research

MAIA exemplifies a significant stride in interpretability research by shifting some interpretive burdens from humans to machines, potentially speeding up the understanding of complex AI models. Practical applications range from improving model transparency to assisting in regulatory compliance by providing understandable insights into model behaviors. Theoretically, the integration of modular design in interpretability tools, as demonstrated by MAIA, can help in iteratively refining these tools, pushing the boundaries of what automated systems can achieve in terms of interpretability and system auditability.

The success and limitations observed in MAIA also guide future research directions, including the improvement of image synthesis models to reduce errors in experimentation, and the enhancement of reasoning capabilities in LLMs to minimize human steering requirements.

Speculations on Future Developments

Considering the evolving nature of generative AI and LLMs, future versions of interpretability agents like MAIA could see improvements in autonomous functionality, requiring less human oversight and achieving higher levels of accuracy and reliability. Integration with more advanced multimodal models may further enhance these systems' capability to understand and interact with a broader range of neural network architectures and types.

In conclusion, while MAIA represents a progressive step towards automated model interpretability, its dependence on human confirmation and supervision underlines the complexity and challenges of fully automating the interpretability of AI systems. Nonetheless, MAIA sets a foundational framework upon which more sophisticated and autonomous systems might be developed, promising enhanced transparency and accountability in AI applications.