Showing 49–60 of 64 articles
Participation changes the question and improves the answer before sharing it with the whole community: Citizenship and networks
A useful consultation does not seek to confirm a decision already made: it changes questions, criteria, and answers so the community can see what changed.
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After launch, the important work begins when an app asks for information: Privacy and data
Launch does not close data decisions. An app must observe failures without turning every use into a profile, explain changes, and correct permissions when real service reveals different needs.
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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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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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Why this also affects people who do not use the technology: Creativity, culture and authorship
Decisions about creative tools change cultural choice, information, rights and opportunities across the whole community.
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The human side of an automated decision: Creativity, culture and authorship
Automated recommendations and filters can distribute attention and cultural opportunities; this is why they need understandable rules and human review.
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The detail that transforms a news item into a public problem: Work when a system makes the decisions
A tiny algorithmic penalty can become a public problem when it is repeated thousands of times and challenging it costs more than the loss. A guide to measuring scale, repairing the pattern and restoring human control.
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The myth that makes the debate harder: Work when a system decides
The idea that a machine decides without bias confuses consistency with justice. This guide dismantles the myth and offers questions, tests and safeguards for evaluating workplace algorithms.
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The warning sign we should not ignore: Work when a system makes the decisions
When a score affects shifts, recruitment or pay and nobody can explain it, a warning sign appears. A guide to documenting, questioning and seeking meaningful human review.
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The human side of an automated decision: Work when a system makes the decisions
A guide to recognising when an algorithm shapes employment, demanding meaningful human review and protecting dignity, equality and the ability to challenge decisions.
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The discussion missing from the headlines: Work when a system makes the decisions
A practical guide to understanding how systems allocate shifts, monitor, score and shape employment decisions, and which safeguards protect health, dignity and the right to challenge errors.
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The promise worth reading carefully: Work when a system decides
Artificial intelligence promises faster, more objective employment decisions. This guide shows how to turn sales claims into evidence, measure errors, demand meaningful human review, and protect affected people.
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