The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.

We need to manage transparency and control while using AI, ensuring users can trust the AI's actions on their behalf.

Transparency during the development of AI is crucial to prevent concentration of power and ensure alignment with human values.

AI's algorithms can have false positives and negatives, so it's crucial to balance sensitivity and specificity in medical applications.

The AI community is actively addressing issues of fairness, accountability, and transparency, which are crucial for the responsible deployment of AI technologies.

AI systems struggle to accurately verify information, which can lead to significant issues in critical fields like medicine, where incorrect diagnoses can have serious consequences.

There's a significant amount of ongoing research and pilot projects in AI for healthcare, but many technologies have not yet been widely rolled out due to issues of interpretability and fairness that need to be addressed first.

Transparency in AI development, including whistleblower protections and safety case publications, is essential for accountability.

Achieving 100% accuracy in AI applications is crucial, especially in legal contexts where providing incorrect information can have serious consequences for lawyers handling important cases.