mlsanityData to 7 Oct 2026

False perfection in machine prediction: Detecting and assessing circularity problems in machine learning

arXiv 2106.12417 ↗First posted 23 Jun 2021cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper is an excerpt of an early version of Chapter 2 of the book "Validity, Reliability, and Significance. Empirical Methods for NLP and Data Science", by Stefan Riezler and Michael Hagmann, published in December 2021 by Morgan & Claypool. Please see the book's homepage at https://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1688 for a more recent and comprehensive discussion.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 44 references that are in the map. First posted on arXiv 23 Jun 2021.

Builds on

11 of 44 references.

References · 11

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  4. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
  5. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 318
  6. 2019Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biasescited by 279
  7. 2018Learning Not to Learn: Training Deep Neural Networks with Biased Datacited by 255
  8. 2017Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillationcited by 62
  9. 2020Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and modelscited by 11
  10. 2019Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Predictioncited by 5
  11. 2020Embedding Meta-Textual Information for Improved Learning to Rankcited by 2

Built on by

Not cited yet.