Biased Sample

Example(s):
- Facial-Recognition Dataset that draws predominantly from white men.
- …
See: Data Bias, Missing-not-at-Random Dataset, Unbiased Sample, Biased Dataset, Confirmatory Effect, Placebo Effect.

Rewferences

(Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Sampling_bias Retrieved:2021-5-19.
- In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others. It results in a biased sample of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling. Medical sources sometimes refer to sampling bias as ascertainment bias. Ascertainment bias has basically the same definition, but is still sometimes classified as a separate type of bias.

https://www.fastcompany.com/90636406/aspiring-to-zero-bias-in-ai
- QUOTE: Sample bias: Sample bias — or selection bias — is a data set that doesn’t reflect the diversity of the environment in which the machine-learning model is going to be run. An example is when a facial-recognition system data set draws predominantly from white men. An algorithm trained from this data set will struggle to recognize women and people of different ethnicities.