Out-group homogeneity bias
- See also: Machine learning terms
Out-group Homogeneity Bias
Out-group homogeneity bias, also known as the out-group homogeneity effect, refers to the cognitive bias that leads individuals to perceive members of an out-group, or those that do not belong to their own social or cultural group, as more similar to one another than they actually are. This bias can manifest in various social, cultural, and demographic contexts, including ethnicity, nationality, gender, and age. The phenomenon is often contrasted with the in-group bias, where individuals view members of their own group as more diverse and complex.
Origins and Mechanisms
The out-group homogeneity bias is rooted in a combination of cognitive and motivational processes. On the cognitive level, individuals have limited cognitive resources, which can lead to simplification and generalization when processing information about others. This is particularly true when individuals have less exposure or familiarity with members of an out-group, as they are more likely to rely on stereotypes to make sense of the group's characteristics.
On the motivational level, the out-group homogeneity bias can be seen as a means of preserving one's positive self-image and social identity. By perceiving out-group members as more homogenous, individuals can more easily differentiate themselves and their group from others, thereby enhancing their group's distinctiveness and reinforcing their sense of belonging and self-worth.
Implications for Machine Learning
In the context of machine learning, the out-group homogeneity bias can have significant implications for the fairness and accuracy of models, particularly those that rely on human-generated data or human-like processing. Machine learning algorithms can inadvertently learn and propagate this bias if the data they are trained on reflects the biased perceptions of their human creators. This can lead to biased decision-making, as well as the reinforcement of existing stereotypes and social inequalities.
Bias in Machine Learning provides an overview of the various ways that biases can emerge and affect machine learning models, including through biased training data, model assumptions, and evaluation metrics.
Explain Like I'm 5 (ELI5)
Out-group homogeneity bias is when people think that members of a group they don't belong to are all the same, while people in their own group are different from one another. This can happen because people don't know much about other groups, or because they want to feel good about their own group. In machine learning, this bias can cause problems if the computer learns from data that has this bias. It can make the computer program unfair or make wrong decisions based on the bias.