The practice of developing AI systems that are fair, transparent, accountable, and respect human rights.
The practice of developing AI systems that are fair, transparent, accountable, and respect human rights. Covers bias mitigation, privacy protection, environmental impact, and equitable access. Different from AI safety, which focuses more on technical risk and control. An increasingly important consideration for deployment.
In AI, bias has two meanings.
The broad field studying how to build AI systems that are safe, reliable, and beneficial.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
The research field focused on making sure AI systems do what humans actually want them to do.
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