Concern 428 · Working draft · editorial review in progress
Pregnancy inference
Pregnancy inference is a guess about pregnancy or reproductive circumstances based on indirect data such as shopping or browsing patterns.
Understanding the concern
A retailer or advertising system may combine ordinary activities into a sensitive prediction. Those activities can have many explanations, including buying for another person. Even a correct prediction can reveal information before someone chooses to share it. An incorrect one can be distressing or expose assumptions about health and family life.
The frequently repeated story of a retailer detecting pregnancy should not be treated as proof that every modern AI can do this reliably. The broader, documented issue is commercial profiling and sensitive-data sharing. Users need to understand inferred categories, not just the raw purchase history they knowingly supplied.
An example
Personalised offers reveal a suspected reproductive circumstance on an account or device shared with others.
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.
- 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
These sources establish profiling and sensitive-location concerns. They do not verify a universal pregnancy predictor or the details of a popular retailer anecdote.
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.
Federal Trade Commission · Enforcement announcement
FTC Finalizes Order with X-Mode and Outlogic ↗ (opens in a new tab)2024 settlement concerning sensitive location information, including clinics and places of worship.
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
Are reproductive inferences created or shared, and can people prevent that use of ordinary activity data?
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
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.