Intermediate · Research
In-group bias
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Favoring one's own group or characteristics, potentially leading to biased evaluations or data in machine learning.
Technical Definition
Favoring one's own group or characteristics, potentially leading to biased evaluations or data in machine learning.
How it works
In-group bias is the tendency to show preference towards individuals who are part of one's own group or share similar characteristics. In the context of ML, this can manifest if the data labelers or testers predominantly come from the same background as the developers, potentially skewing results and invalidating the testing process. It's a form of group attribution bias.
Related Concepts
- Fairness — The principle that AI systems should treat individuals and groups equitably.
- Bias (ethics/fairness) — Unfair prejudice or favoritism towards certain groups or things, which can influence data, system design, and user interactions.
- Responsible AI (RAI) — A holistic framework encompassing the ethical, legal, and societal implications of AI, promoting trustworthy and beneficial systems.