Tag : AI ethics

Increasing public trust in AI is a complex challenge that requires transparency, ethical development, accountability, data privacy, bias reduction, trustworthiness, public education, collaboration, and possible regulation. Trust must be earned and maintained through ongoing effort.

AI model optimization makes AI models faster, smaller, and more efficient by improving performance and reducing resource requirements. Key techniques include pruning, quantization, knowledge distillation, and hyperparameter tuning. This enables deployment on resource-constrained devices and reduces costs.

AI exacerbates inequality by benefiting privileged groups while marginalized communities face barriers to access, justice, and opportunity.

Sustainable AI means developing and using artificial intelligence in a way that minimizes its negative environmental, social and ethical impacts and maximizes its positive contributions to a sustainable future.

AI literacy is the ability to understand, use, and critically evaluate artificial intelligence systems, including their benefits, risks, and ethical implications.

Diverse stakeholders (governments, businesses, civil society, etc.) must collaborate in the development and implementation of AI to address ethical concerns, build trust, foster innovation, and ensure the benefit of AI for all humanity.