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

Abstract

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.

Tasks

Reproductions