Organizations must adapt their performance metrics to account for potential biases and undesired behaviors in AI systems over time.

AI technology is rapidly improving, with current systems being less biased and more fair than they were six months ago, although challenges with bias and fairness still exist.

As AI evolves, it will be crucial to ensure that it does not perpetuate biases and that it can appreciate absolute truths rather than just reflecting human biases.

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

The internet's inherent biases can influence the data used to train AI models, leading to skewed representations that are difficult to correct.

We must be cautious about how we define metrics for AI systems to avoid unintended consequences, such as optimizing for the wrong outcomes.

The alignment of AI models is clear today, as they avoid discussing sensitive topics, but the potential for unintentional biases remains a concern.

An AI government could potentially make unbiased decisions based on collective human preferences, but there are significant challenges in ensuring that such a system remains free from external influences.

The challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.