Concern 761 · Working draft · editorial review in progress

Data extraction from Global South

Unfair data extraction occurs when people's information or cultural knowledge is used to create AI value while they have little say in the use or share in the benefits.

Understanding the concern

Data can move across borders much more easily than bargaining power. A community may supply speech, images or local knowledge while decisions about access and commercial use are made elsewhere. Permission to view material online is not the same as a meaningful agreement about every later use. The concern includes consent, control and the ability to benefit from resulting tools.

Development research highlights unequal access to data, infrastructure and skills. The issue is like taking seeds from a community garden to build a business while excluding its gardeners from decisions. Fair arrangements can include clear agreements, local leadership and useful returns, rather than assuming that collecting more material automatically helps the people it describes.

An example

A language dataset supports a commercial product, but its contributing community cannot access the resulting tool or influence how the language is represented.

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.

Evidence and its limits

The report provides a development framework. The community example illustrates a possible imbalance rather than documenting a specific extraction case.

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Broader frameworks and background

These resources provide context. They do not establish every claim in this concern.

A question to discuss

What choices and benefits do the people who supplied this knowledge retain?

Keep track of different views, what needs checking, and any next step in your own discussion notes.

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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.

Justice

Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.

Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.

Ask together: Who benefits, who bears the cost, and who can challenge the decision?

Solidarity

Solidarity means standing with people whose lives are affected, especially when they have less power to protect their interests. It turns concern into a willingness to share responsibility.

Include affected people in decisions and support practical remedies when something goes wrong. Do not leave the cost of learning or repair entirely with those who were harmed.

Ask together: What would it mean to stand with the people carrying the greatest burden?

Service

Service means using abilities and resources to meet real needs. It asks whether the people meant to benefit actually receive help, rather than treating them as a means to another goal.

Speak with the people the project is meant to serve. Notice burdens it may add, such as extra checking or confusing forms. Judge success by their experience as well as the organization's targets.

Ask together: Who is this meant to serve, and do they experience it as helpful?

Editorial ratings

These are editorial judgments from the register, not measured probabilities or predictions about your situation.

Severity
Serious · 3/5
Likelihood
Widely observed · 4/5
Time horizon
Now
Register risk score
12
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