If a model encodes true reliability asymmetries in localized meta-explanations, how does making those asymmetries decay over time unless reconfirmed by fresh evaluations (e.g., automatically softening “English is more tested than X” statements after N months) affect long-term perceptions of fairness and over-trust, compared with static, never-updated asymmetry statements?

Answer

Making reliability-asymmetry statements decay over time unless reconfirmed tends to improve long‑run perceptions of procedural fairness and reduce entrenched over‑trust patterns compared with static, never‑updated statements—provided the decay is tied to transparent, periodic re-evaluation and is implemented gradually rather than abruptly.

• Fairness perceptions: Users are more likely to see the system as fair and non‑biased when asymmetry statements are explicitly time‑bounded and periodically re‑checked, instead of permanently asserting that one language is better. This signals that reliability gaps are contingent on monitoring and improvable, not essential features of the language.
• Over‑trust calibration: Time‑decayed asymmetries help prevent stale warnings (“English is more tested than X”) from locking in an exaggerated or outdated reliance gap once the weaker language improves. As asymmetry cues are softened or removed after N months without evidence of a large gap, users gradually update their reliance toward the now‑more‑accurate reality, reducing miscalibration in both directions.
• Compared with static statements: Static, never‑updated asymmetry meta-explanations are initially good at reducing dangerous over‑trust in the weaker language (de90b065‑…), but over time they (a) risk overstating the gap if the weaker language catches up, and (b) can start to look like permanent linguistic bias. A decay‑and‑refresh scheme preserves the transparency and calibration benefits of explicit asymmetries while limiting long‑term stigma and outdated routing behavior.

This works best when: (i) each asymmetry cue is explicitly labeled as based on evaluations as of a given period, (ii) the decay is smooth (e.g., from strong to mild to neutral wording) unless new data reconfirms the gap, and (iii) any reconfirmation is clearly grounded in recent monitoring rather than left implicit.