Summary of No Free Delivery Service: Epistemic Limits Of Passive Data Collection in Complex Social Systems, by Maximilian Nickel
No Free Delivery Service: Epistemic limits of passive data collection in complex social systems
by Maximilian Nickel
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (stat.ML)
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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 conventional train-test paradigm in machine learning and AI. While rapid model validation has driven progress in these fields, modern AI systems often rely on complex tasks and data collection practices that undermine test validity. The authors demonstrate that for widely used inference settings in social systems, the train-test paradigm is invalid with high probability. This epistemic issue affects key AI applications, including recommender systems and large language models. The paper illustrates these results using the MovieLens benchmark and discusses implications for AI in social systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper questions how we test artificial intelligence (AI) models to make sure they are working correctly. Right now, we often use a method called train-test that can’t guarantee accurate results because it’s based on assumptions that don’t always hold true. The authors show that this approach is flawed for many AI applications and highlight the importance of having valid tests to ensure AI systems have positive social impacts. |
Keywords
» Artificial intelligence » Inference » Machine learning » Probability
