Concern 95 · Working draft · editorial review in progress
Data-quality drift
Data-quality drift is a change in the completeness, format, meaning, or reliability of information flowing into a model.
Understanding the concern
A field may change units, a sensor may stop reporting, or a supplier may rename a category. The system may continue accepting the data even though it no longer means what the model expects.
This differs from a real change in the world. The apparent change may be a broken pipeline rather than a new customer behavior. Checks should examine the data's structure and meaning before treating every surprise as a reason to retrain.
An example
A temperature feed switches from Celsius to Fahrenheit while keeping the same column name.
Illustrative unless identified as a documented case.
Words in plain English
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Drift
- A change over time in data or real-world conditions that can make a system less reliable.
Evidence and its limits
Supports tracking data dependencies, assumptions, and changes; the units example is illustrative.
Sculley and colleagues · NeurIPS 2015 · Research framework
Hidden Technical Debt in Machine Learning Systems ↗ (opens in a new tab)Explains maintenance problems, hidden dependencies, feedback loops and changes in machine-learning systems.
Gebru and colleagues · Communications of the ACM · Research proposal
Datasheets for Datasets ↗ (opens in a new tab)Documentation questions cover composition, collection, time, uses, permissions, and maintenance. This is a documentation method, not proof of a particular dataset's defects.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Did reality change, or did the way we record it change?
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.
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
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?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
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
Search for more writing
For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.