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The problem invalidates some 3/4s of machine learning studies according to ... but 28% claimed to know of colleagues who engaged in questionable research practices. ...
HARKing (hypothesizing after the results are known) is an acronym coined by social psychologist Norbert Kerr [1] that refers to the questionable research practice of "presenting a post hoc hypothesis in the introduction of a research report as if it were an a priori hypothesis".
Using machine learning to detect bias is called, "conducting an AI audit", where the "auditor" is an algorithm that goes through the AI model and the training data to identify biases. [161] Ensuring that an AI tool such as a classifier is free from bias is more difficult than just removing the sensitive information from its input signals ...
Another aspect of the conditioning of statistical tests by knowledge of the data can be seen while using the system or machine analysis and linear regression to observe the frequency of data. [clarify] A crucial step in the process is to decide which covariates to include in a relationship explaining one or more other variables.
Questionable research practices uncover a large grey area of problematic practices, which are frequently associated to deficiencies in research transparency. In 2016, a study identified as much as 34 questionable research practices or "degree of freedom", that can occur at all the steps of the project (the initial hypothesis, the design of the ...
For example, in 2017, Google researchers used the term to describe the responses generated by neural machine translation (NMT) models when they are not related to the source text, [22] and in 2018, the term was used in computer vision to describe instances where non-existent objects are erroneously detected because of adversarial attacks. [23]
Advanced Lectures on Machine Learning. Lecture Notes in Computer Science. Vol. 3176. pp. 169– 207. doi:10.1007/b100712. ISBN 978-3-540-23122-6. S2CID 431437; Bousquet, Olivier; Elisseeff, Andr´e (1 March 2002). "Stability and Generalization". The Journal of Machine Learning Research. 2: 499– 526.
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