Against one group of people, or one side in an argument, often not based on fair judgement.

Confirmation Bias involves favoring or focusing on information that confirms your existing beliefs and preconceptions. You may automatically discount information that does not confirm your bias. [1]

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.[4]

Labelling bias occurs when the set of labeled data is not fully representative of the entire universe of potential labels. This is a very common problem in supervised learning, stemming from the fact that data often needs to be labeled by hand (which is difficult and expensive).[3]

Gender Bias is a preference for one gender over the other. Gender bias is usually a result of an individual’s ingrained beliefs about gender roles and stereotypes Contextual bias[1]

Conscious biases are prejudices. They usually discriminate against people or groups of people. There is usually malicious intent involved in conscious biases. [1]

Unconscious or Implicit bias is related to implicit stereotypes and is when you unconsciously attribute certain qualities to certain social groups. [1]

Statistical Bias is related to the process of data collection. Statistical bias can affect the way a research sample is selected or the way that data is collected. It can result in misleading results that differ from the accurate representation. [1]

Racial Bias is form of implicit bias, which refers to the attitudes or stereotypes that affect an individual's understanding, actions, and decisions in an unconscious manner. [1]

Association Bias is the tendency to be easily influenced by associations. Machine Learning Bias/ ai bias: AI bias is an anomaly in the output of machine learning algorithms, due to the prejudiced assumptions made during the algorithm development process or prejudices in the training data. [1]

Measurement bias occurs when choosing, collecting, or computing features and labels to use in a prediction problem.[2]

Aggregation bias arises when a one-size-fits-all model is used for data in which there are underlying groups or types of examples that should be considered differently. [2]

Learning bias arises when modeling choices amplify performance disparities across different examples in the data. [2]

Evaluation bias occurs when the benchmark data used for a particular task does not represent the use population. [2]

Deployment bias arises when there is a mismatch between the problem a model is intended to solve and the way in which it is actually used. [2]

Historical bias arises even if data is perfectly measured and sampled, if the world as it is or was leads to a model that produces harmful outcomes. Such a system, even if it reflects the world accurately, can still inflict harm on a population. Considerations of historical bias often involve evaluating the representational harm (such as reinforcing a stereotype) to a particular group. [2]

Representation bias occurs when the development sample under-represents some part of the population, and subsequently fails to generalize well for a subset of the used population. [2]

Recall bias is a systematic error caused by differences in the accuracy or completeness of the recollections retrieved.by study participants regarding events or experiences from the past. [4]

Also known as machine bias, statistical bias or model bias, to which theNooscope diagram gives particular attention) is the further amplification of historical bias and dataset bias by machine learning algorithms.

Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed.[4]