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Experimenter's bias

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TL;DR. Bias introduced by the experimenter's expectations influencing the results.

Technical Definition

Bias introduced by the experimenter's expectations influencing the results.

How it works

Experimenter's bias, also known as confirmation bias, occurs when the researcher's expectations or preconceived notions unconsciously affect the outcome of an experiment. This can lead to skewed results that appear to support the hypothesis, even if it's not accurate.

Related Concepts

  • 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.
  • Responsible AI (RAI) — A holistic framework encompassing the ethical, legal, and societal implications of AI, promoting trustworthy and beneficial systems.

Further Reading

  • Google ML Glossary