Concern 140 · Working draft · editorial review in progress
Missing evaluations for harms
Missing harm evaluations are gaps in testing that leave important social or practical consequences unexamined.
Understanding the concern
A system may be tested for answer accuracy while nobody examines exclusion, misleading persuasion, worker burden, or the ability to challenge a decision. Some effects appear only through use over time.
No single benchmark can represent every community or harm. The aim is to identify relevant questions and combine methods, including participation by affected people. Absence of a test result is not evidence that a risk is absent.
An example
A tool passes a language test but is never checked for whether users can correct a harmful mistake about themselves.
Illustrative unless identified as a documented case.
Words in plain English
- Benchmark
- A standard test used to compare systems. It only measures the tasks and conditions included in that test.
Evidence and its limits
Supports lifecycle-wide evaluation and contextual harm analysis; it does not claim that no standards exist for any social risk.
NIST · AI 600-1 · 2024 · Official risk framework
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ↗ (opens in a new tab)Describes generative-AI risk categories and management actions; it does not measure the failure rate of every product.
Suresh and Guttag · EAAMO 2021 · Research framework
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle ↗ (opens in a new tab)Framework distinguishes historical, representation, measurement, aggregation, learning, evaluation, and deployment bias. These categories can overlap.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Which important effects on people have we not tested at all?
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
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