The workshop proceedings document discusses confidence-building measures for artificial intelligence. These measures aim to improve the reliability and trustworthiness of AI systems by assessing their performance and providing feedback on their limitations. The paper outlines various techniques, including uncertainty estimation and calibration, to help developers build more robust and transparent AI models. --- Why it matters: This matters because it addresses a key challenge in AI development: ensuring that AI systems are reliable and trustworthy. By understanding how to measure and improve AI confidence, researchers can develop more accurate and dependable AI models. Source: https://openai.com/index/confidence-building-measures-for-artificial-intelligence