Summary of From Model Explanation to Data Misinterpretation: Uncovering the Pitfalls Of Post Hoc Explainers in Business Research, by Ronilo Ragodos et al.
From Model Explanation to Data Misinterpretation: Uncovering the Pitfalls of Post Hoc Explainers in Business Research
by Ronilo Ragodos, Tong Wang, Lu Feng
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Machine learning models have become increasingly used in business research, but their black box nature means that post-hoc explainers are often employed to provide insights. However, we found that these explainers are being misused in business research, with users drawing inferences about the underlying data from the explanations themselves. To investigate this trend, we conducted extensive experiments comparing the two most popular post-hoc explainers, SHAP and LIME, using real-world data from an econometric context. Our findings suggest that these explanations can be unreliable, and we propose mitigation strategies to improve their validity. Ultimately, we caution business researchers against drawing false insights from these explanations and instead advocate for their use in facilitating hypothesis formulation and empirical investigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are being used more and more in business research. However, they’re like black boxes – you can’t really understand how they work. To help with this, people have started using post-hoc explainers to provide insights into the models’ decisions. But we found that many researchers are misusing these explainers by drawing conclusions about the underlying data from the explanations themselves. In our research, we tested two popular explainers, SHAP and LIME, and found that they can be unreliable. We also came up with some strategies to make them more accurate. Our main point is that business researchers should be careful not to draw false conclusions from these explanations – instead, they should use them to help formulate hypotheses and test them empirically. |
Keywords
» Artificial intelligence » Machine learning
