As Ai increasingly affects everyday life, its regulation is not only a matter for governments, institutions and technology companies. Citizens can also influence how AI is governed, although the opportunities...
Behind every AI system is a physical infrastructure that requires energy and resources, making environmental sustainability an important part of the conversation about technological innovation. Balancing the two may involve...
Access to AI is not only about having the latest tools, but also the infrastructure, education and skills needed to use them effectively. Individuals, companies and countries with greater access...
AI systems learn from data created by societies that already contain inequalities and biases. When historical patterns are used to make decisions about hiring, lending, law enforcement or other areas,...
Interacting with AI services often means sharing information with systems whose data practices may not always be obvious to users. Personal details, work-related information or other sensitive data can potentially...
Current AI systems can produce impressive results, but their capabilities still come with important limitations. They depend heavily on data, can generate convincing but incorrect information and may struggle when...
Training AI is not simply a matter of collecting as much data as possible. The information used needs to be accurate, relevant, representative and well structured, because models learn patterns...
Today's AI can produce remarkably human-like responses, but that does not mean it learns or thinks in the same way people do. AI systems identify patterns in data and use...
Explaining AI to a child means turning a complex technology into something familiar and understandable. It can be described as a smart digital helper that learns patterns from many examples...
One of the biggest misunderstandings about AI is assuming that it thinks, understands or produces reliable answers independently of human judgement. Its convincing outputs can encourage people to trust it...
