AI safety, also known as artificial intelligence safety, is the interdisciplinary field of research and practice focused on ensuring the safe, beneficial, and ethical development and deployment of artificial intelligence (AI) systems. It addresses concerns such as unintended consequences, bias, accountability, and control in AI algorithms and autonomous systems, aiming to mitigate risks and maximize societal benefits. Readers can explore AI safety principles, frameworks, and governance mechanisms for promoting responsible AI development, ensuring alignment with human values and interests, and addressing potential risks and challenges.

AI Safety

Meet Inspect: The Latest AI Safety Evaluations Platform Introduced By UK’s AI Safety Institute

U.K. agency releases tools to test AI model safety

Snowflake Cortex LLM: New Features & Enhanced AI Safety

Machine Unlearning in 2024

Deciphering Transformer Language Models: Advances in Interpretability Research

AI Safety: Ensuring the Responsible Development of Artificial Intelligence

NIST launches a new platform to assess generative AI

This AI Paper from MLCommons AI Safety Working Group Introduces v0.5 of the Groundbreaking AI Safety Benchmark

Import AI 368: 500% faster local LLMs; 38X more efficient red teaming; AI21's Frankenmodel

India, grappling with election misinfo, weighs up labels and its own AI safety coalition