Recency Bias
Definition
Recency bias overweights new observations excessively.
The systematic overweighting of newer observations compared to older, possibly equally relevant information.
Simply explained
The last impression counts too much, although older information might still be equally important.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A forecast reacts excessively to the last weak month and ignores long-term seasonality.
Limits and pitfalls
- Calling every recency weighting a bias or preferring old information in truly drifting data.
Origin & history
The term originates from psychology and decision research and is also used for rankings and analyses.
Use in mid-sized companies
- Possible application: A forecast reacts excessively to the last weak month and ignores long-term seasonality.
Frequently asked questions
What does recency bias mean?
The systematic overweighting of newer observations compared to older, possibly equally relevant information. What mistakes should be avoided? Calling every recency weighting a bias or preferring old information in truly drifting data.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: