Temporal gap risk for AI
Description
Temporal gaps in synthetic data refer to the discrepancies between the constantly evolving real-world data and the fixed conditions that are captured by synthetic data. Temporal gaps potentially cause synthetic data to become outdated or obsolete over time. Gaps arise because synthetic data is generated from seed data that is tied to a specific point in time, which limits its ability to reflect ongoing changes.
Why is temporal gap a concern for foundation models?
Temporal gaps in synthetic data can render it outdated or obsolete as real-world conditions evolve, leading to mismatches between current realities and the assumptions embedded in the data, potentially compromising model performance, accuracy and relevance. However, in prioritizing current realities, there is also a risk of erasing historical or cultural contexts, highlighting the need to strike a balance between adapting to evolving conditions and preserving valuable knowledge and perspectives.
Parent topic: AI risk atlas
We provide examples covered by the press to help explain many of the foundation models' risks. Many of these events covered by the press are either still evolving or have been resolved, and referencing them can help the reader understand the potential risks and work toward mitigations. Highlighting these examples are for illustrative purposes only.