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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".
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.
[2] [3] [4] However, researcher degrees of freedom can lead to data dredging and other questionable research practices where the different interpretations and analyses are taken for granted [5] [6] Their widespread use represents an inherent methodological limitation in scientific research, and contributes to an inflated rate of false-positive ...
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. [162] 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 ...
In 21 surveys of academics (mostly in the biomedical sciences but also in civil engineering, chemistry and economics) carried out between 1987 and 2008, 2% admitted fabricating data, but 28% claimed to know of colleagues who engaged in questionable research practices. [2]
The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. [36] Some open-sourced tools are looking to bring more awareness to AI biases. [37]
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 ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]