Concern 285 · Working draft · editorial review in progress
Reproducibility failures
Reproducibility failures occur when repeating an AI experiment or deployment does not produce the expected result because important conditions were not controlled or recorded.
Understanding the concern
Random choices, hardware, software versions, and data preparation can all affect outputs. Some variation is expected, but unexplained differences make it hard to verify a claimed improvement or investigate a failure. Saving only the final model is often insufficient.
A fixed random seed does not guarantee identical results across every platform or library release. Reproducibility needs a record of the relevant environment and a clear tolerance for variation. The aim is to understand which conclusions remain reliable, not to promise impossible exactness in every setting.
An example
Two cooks follow the same recipe but use different ovens and unrecorded ingredient substitutions, then cannot explain the different result.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
PyTorch documents limits across releases and platforms and methods for reducing nondeterminism; other frameworks have their own details.
PyTorch · version 2.14 documentation · Official technical documentation
Reproducibility ↗ (opens in a new tab)Explains nondeterminism and limits on reproducibility across releases, platforms and devices, with methods to reduce variation.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
Could another team reproduce the important finding from the records we preserved?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Cooperation
Cooperation means working with others toward a shared purpose while respecting their contribution. It is not simply asking people to accept a decision already made.
Share information, roles, and credit. Include the people who do the daily work when choosing or testing a tool. Make disagreements discussable so that cooperation does not become pressure to agree.
Ask together: What can we do together that none of us can do well alone?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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