How do I lay a reliability foundation for an AI model's output?
You lay it by showing the method is reliable and was reliably applied — documenting the model, its validation, the inputs, error rates or performance, and that the specific run is reproducible — mirroring FRE 702(b)-(d) and the direction of proposed FRE 707.…
Answer.
You lay it by showing the method is reliable and was reliably applied — documenting the model, its validation, the inputs, error rates or performance, and that the specific run is reproducible — mirroring FRE 702(b)-(d) and the direction of proposed FRE 707. Reproducibility and tamper-evidence are persuasive elements. Planisphere supplies that backbone: a fixed probe set, recorded metrics, and a sha-pinned record a third party can recompute. It strengthens the foundation; the court rules on admissibility, not Planisphere.
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machine-generated evidence · Rule-702-grade reliability for AI output (PROPOSED).
More on FRE 707 (proposed).
Try FRE 707 (proposed) with a free test key.
Start with the free sandbox. Planisphere measures model behaviour and emits a reproducible, sha-pinned record — it does not certify, file, or give legal advice.