Showing 1–12 of 12 articles
Participation changes the question and improves the answer around an automatic response: Everyday artificial intelligence
Participation turns an automatic response into a service that can be corrected: it makes room to identify errors, add context, and decide what improves without shifting every burden to the recipient.
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Care also means not monitoring everything around an automatic response: Everyday artificial intelligence
Care around an automatic response does not mean recording every action: it means limiting data, providing a human route, and reviewing only what is needed to correct harm.
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Supervision requires the power to stop and correct an automatic response: Everyday artificial intelligence
Supervising AI is not watching a screen: it requires authority to stop a response, correct its effects, and explain the change to affected people.
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After launch, the important work begins — before an automatic response: Everyday artificial intelligence
Launching an AI tool does not close a project: it starts error observation, support for affected people, and verifiable improvement of every automatic response.
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Safety begins when reporting an error is possible — before an automatic response: Everyday artificial intelligence
The safety of an automatic response depends on errors being reportable, correctable, and explainable without punishment. Everyday AI needs clear channels, human oversight, and repair.
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What works for most people can exclude someone — before an automatic response: Everyday artificial intelligence
An automatic response designed for the majority case can leave out people who write differently, need more time, or do not fit its assumptions. Everyday AI requires alternatives and human review.
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A rumor travels fast when it tries to care — before an automatic response: Everyday artificial intelligence
Rumours about AI responses often begin in care, but they can turn a doubt into alarm. A responsible response verifies, explains limits, and keeps a human route open.
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Conflict is not resolved by adding another screen — before an automatic answer: Everyday artificial intelligence
When an automatic answer conflicts with a real need, adding steps and screens usually hides the problem. What is needed is explanation, review, and a human route with power to decide.
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The exception reveals the quality of the system — before an automatic answer: Everyday artificial intelligence
Automatic answers often work for anticipated cases. Their quality is revealed when someone does not fit: an exception should open understanding, review, and a real human route.
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A useful number can still lead us to the wrong place — before an automatic answer: Everyday artificial intelligence
A metric can organise work while also hiding what matters. With automatic answers, sound measurement requires looking at decisions, harm, and alternatives, not only speed or acceptance.
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Accepting does not always mean being able to choose — before an automatic answer: Everyday artificial intelligence
Accepting an automatic answer does not, by itself, show that a free and informed choice was available. A suggestion from an assistant, a classification on a screen, or an answer generated during an application can arrive
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Manifesto: the first question must arrive before the tool
A responsible automated response starts with the question that protects people, not with the tool that promises speed.
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