Concern 198 · Working draft · editorial review in progress
Explanation as manipulation surface
Explanation as a manipulation surface means using a convincing explanation to create trust or hide a problem rather than reveal how a decision works.
Understanding the concern
A developer can choose what an explanation emphasizes, and a model can generate reasons that sound reassuring. Research shows that some post-hoc methods can be fooled into presenting an innocuous picture of a biased classifier. The explanation itself therefore deserves scrutiny.
This differs from an innocent mistake in an explanation tool because the presentation may be deliberately shaped to mislead. Independent checks, access to relevant evidence, and a way to challenge conclusions help prevent explanations from becoming a form of advertising.
An example
A report displays a carefully selected chart that makes a bad decision look reasonable while omitting the decisive factor.
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.
Evidence and its limits
The attack study demonstrates manipulated explanations under specified conditions; unfaithful-reasoning research shows a related but not necessarily deliberate failure.
Slack and colleagues · 2020 · Research study
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods ↗ (opens in a new tab)Demonstrates manipulated explanations in tested perturbation-based methods and settings.
Turpin and colleagues · NeurIPS 2023 · Research study
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting ↗ (opens in a new tab)Shows explanations omitting experimentally introduced influences in specified models and tasks.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What independent evidence would reveal whether this explanation is concealing something important?
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.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
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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