Summary of Missed Causes and Ambiguous Effects: Counterfactuals Pose Challenges For Interpreting Neural Networks, by Aaron Mueller
Missed Causes and Ambiguous Effects: Counterfactuals Pose Challenges for Interpreting Neural Networks
by Aaron Mueller
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper challenges the assumptions underlying most interpretability research, specifically the reliance on counterfactual theories of causality. Current methods manipulate model inputs or components, observing changes in output logits or behaviors. While this approach provides more accurate evidence than correlation-based methods, it has inherent biases that affect findings. The authors highlight two key issues: (i) multiple independently sufficient causes are often missed due to the limitations of counterfactual theories; and (ii) non-transitive dependencies in neural networks complicate extracting and interpreting causal graphs. This paper’s findings have significant implications for interpretability researchers, leading to proposed suggestions for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that we’re correctly understanding how models are working. Right now, many researchers use a certain way of testing models called counterfactuals. While this method gives us more accurate results than just looking at patterns, it’s not perfect. There are two main problems with counterfactuals: (i) they often miss important causes that contribute to the same effect; and (ii) neural networks have dependencies that make it hard to understand how they’re working. The authors of this paper point out these issues and suggest ways for researchers to improve their work. |
Keywords
* Artificial intelligence * Logits
