Concern 430 · Working draft · editorial review in progress
Addiction inference
Addiction inference is the assignment of a substance-use or compulsive-behaviour label from indirect commercial or behavioural data.
Understanding the concern
Purchases, website visits or location patterns may be interpreted as signs of a problem. But these signals can reflect work, research, helping someone else or ordinary behaviour. A sensitive label may be used without a clinical assessment and without the person knowing it exists.
The FTC's data-broker work has documented sensitive commercial categories, including health-related interests. A category useful to a marketer is not a diagnosis. The concern includes stigma, exclusion and exploitative targeting, as well as the inability to correct a hidden profile. Support chosen by a person is different from covertly labelling them.
An example
A person researching recovery services for a relative is placed in a sensitive marketing category.
Illustrative unless identified as a documented case.
Words in plain English
- Inference
- Drawing a conclusion from clues. In AI engineering it can also mean running a trained model to produce an output.
Evidence and its limits
The broker report documents historical categories, not validated addiction diagnoses. The example is hypothetical and avoids claiming any purchase proves addiction.
Federal Trade Commission · Investigation report
Data Brokers: A Call for Transparency and Accountability ↗ (opens in a new tab)2014 commercial data categories and marketing uses; historical examples do not establish current use by every broker.
Research authors; PMC · Research study
Evaluating Generative AI Responses to Real-World Drug-Related Questions ↗ (opens in a new tab)Evaluation of selected models and questions; distinguishes answer quality from observed health outcomes.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- OECD AI Principles (opens in a new tab)
framework
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
Can a person find out about and correct this label, and should it be used for marketing or eligibility decisions 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.
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
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?
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?
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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