Performance reviews often weight recent events far more heavily than they should, making a strong final quarter erase a year of problems or one bad month overshadow sustained excellence—a bias that favors whoever happens to be in favor when review season arrives. Flagging this bias in your own reviews and others' helps you push back when recency is doing unfair work.
Recency bias flagging is a technique where AI analyzes performance review text to determine whether evaluations are disproportionately weighted toward recent events while ignoring a longer track record of contributions and achievements. It compares the time distribution of cited examples against the full review period to expose imbalanced assessments.
This matters because a review that only references your last two months of work may be unfair or strategically framed, and AI can help you build a counter-narrative using your full documented history before a formal meeting or appeal.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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