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Implicit bias
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TL;DR. An unconscious association or assumption that influences perceptions and decisions, potentially affecting data collection and model design.
Technical Definition
An unconscious association or assumption that influences perceptions and decisions, potentially affecting data collection and model design.
How it works
Implicit bias refers to automatic associations or assumptions made by individuals based on their internal mental models and memories. These biases can unintentionally surface in machine learning, influencing how data is collected and categorized, or how algorithms are designed. For instance, assuming a white dress is a universal indicator of a wedding could introduce bias, as this tradition is culturally and historically specific.
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.
- Confirmation bias — The tendency to favor information that confirms existing beliefs, potentially influencing data collection and interpretation in ML.