Summary of A Reply to Makelov Et Al. (2023)’s “interpretability Illusion” Arguments, by Zhengxuan Wu and Atticus Geiger and Jing Huang and Aryaman Arora and Thomas Icard and Christopher Potts and Noah D. Goodman
A Reply to Makelov et al. (2023)’s “Interpretability Illusion” Arguments
by Zhengxuan Wu, Atticus Geiger, Jing Huang, Aryaman Arora, Thomas Icard, Christopher Potts, Noah D. Goodman
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 research paper responds to a recent study by Makelov et al. (2023) on subspace interchange intervention methods like distributed alignment search (DAS; Geiger et al., 2023). The authors argue that these methods can cause “interpretability illusions” and review the technical concept of what an “interpretability illusion” is. They also show that even desirable explanations can be considered illusions, which can lead to rejecting “non-illusory” explanations. The study concludes by highlighting that Makelov et al.’s (2023) examples have contributed significantly to the field of interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at a way to understand how computers learn and make decisions. Some researchers thought they found a problem with this process, but these scientists say it’s not really an issue. They explain what makes something an “interpretability illusion” and show that even good explanations can be misunderstood. The main idea is that the way we train and test computers can cause problems, not the method itself. Overall, this study helps us understand how to make better decisions with computers. |
Keywords
* Artificial intelligence * Alignment
