Towards falsifiable interpretability research (2010.12016v1)

Abstract: Methods for understanding the decisions of and mechanisms underlying deep neural networks (DNNs) typically rely on building intuition by emphasizing sensory or semantic features of individual examples. For instance, methods aim to visualize the components of an input which are "important" to a network's decision, or to measure the semantic properties of single neurons. Here, we argue that interpretability research suffers from an over-reliance on intuition-based approaches that risk-and in some cases have caused-illusory progress and misleading conclusions. We identify a set of limitations that we argue impede meaningful progress in interpretability research, and examine two popular classes of interpretability methods-saliency and single-neuron-based approaches-that serve as case studies for how overreliance on intuition and lack of falsifiability can undermine interpretability research. To address these concerns, we propose a strategy to address these impediments in the form of a framework for strongly falsifiable interpretability research. We encourage researchers to use their intuitions as a starting point to develop and test clear, falsifiable hypotheses, and hope that our framework yields robust, evidence-based interpretability methods that generate meaningful advances in our understanding of DNNs.

• The paper challenges reliance on saliency maps and single-neuron analyses by advocating for hypothesis-driven, falsifiable research methods.
• It critiques current visualization approaches, demonstrating their failure to consistently reflect the true inner workings of deep neural networks.
• It proposes a structured framework to develop and validate interpretability techniques that meet rigorous scientific standards for safety-critical applications.

Towards Falsifiable Interpretability Research: A Critical Analysis

In the domain of interpretability research for deep neural networks (DNNs), the imperative of transitioning from intuition-based methods to those that are empirically robust and falsifiable is increasingly apparent. The paper "Towards Falsifiable Interpretability Research" by Matthew L. Leavitt and Ari S. Morcos serves to critique current practices and propose a methodological shift towards more scientifically rigorous techniques.

Essence of the Paper

The authors argue that the prevalent methods in interpretability research—specifically those relying on saliency maps and single-neuron analyses—often provide an illusion of understanding without delivering substantive insights into DNNs' functioning. These approaches generally emphasize perceptual features tied to individual inputs, yet this reliance on intuitive visualization has led to significant pitfalls, including over-emphasis on singular examples and lack of reproducibility. This work examines these methods through the lens of scientific falsifiability, a standard that postulates a hypothesis must be testable and refutable.

Impediments and Evidence

Two main classes of interpretability methods are scrutinized: saliency-based and neuron selectivity-based methods. The paper identifies certain key limitations and presents two case studies.

1. Saliency Methods: While these methods aim to elucidate which parts of input data are critical for a prediction, they frequently fail under scrutiny. The research highlights the inadequacy of these approaches when subjected to permutation and invariance tests, revealing that many saliency maps do not reflect the model or data’s true nature, often acting similarly to edge detectors rather than reflecting learned priorities within the network.
2. Single-Neuron Based Methods: Often used to infer the functional logic of DNNs, these methods suffer from assumptions about the selective nature of neurons, which may not accurately represent the network's distributed functionality. The authors provide compelling evidence showing how selectivity does not always correlate with task performance, challenging the assumption that understanding individual neurons equates to understanding the network.

Proposed Framework

To combat these issues, the authors present a framework that advocates for interpretability methods constructed around falsifiable hypotheses. They suggest starting with hypotheses grounded in human intuition but emphasize they must be rigorously tested. The paper offers a structured pathway to construct these falsifiable hypotheses and the necessity for robust evaluation frameworks that can scale with varied data samples and complex model architectures.

Implications and Future Directions

The suggestions in this paper direct researchers towards practices that adhere to the scientific method, emphasizing hypothesis testing over speculative assertions. The prospects of such an approach hold potential for developing interpretability tools that support safety-critical applications, such as medical diagnostics, without the risk of misleading practitioners.

The exploration opens avenues for constructing more sophisticated tools that focus on high-dimensional, distributed representations rather than isolated units. Moving forward, techniques should be devised to quantify and verify these representations empirically, ensuring interpretability methods not only provide satisfactory visualization or intuitive grasp but also withstand scientific rigor and reproducibility.

In conclusion, this paper is a clarion call for the interpretability community, advocating for a pivot towards models and methods that are scientifically robust. Such transformation is crucial for establishing trust and reliability in AI systems, especially those deployed in areas with significant societal impact.