Hi Marshall,

I really appreciate your comment, apologies for the delay in my response. I see the ethical implications revolving around three main areas:

Firstly, the dynamic interaction between AI and users raises concerns about data privacy and consent. AI systems learn from user input, therefore it's crucial to ensure that data is used ethically with explicit consent, sadly I think that ship has sailed away a long time ago. Users need to be informed about how their data is used and the extent to which it influences the AI's learning process. As we know with most language models, that's very difficult to execute.

Then there's the issue of bias and fairness. AI systems, being products of their training data, can perpetuate and even amplify biases present in the input they receive from users. Anything using a black box algorithm does this, bias is implied during training . Ensuring that these systems are fair and unbiased requires constant vigilance, diverse data sets, and mechanisms to detect and correct bias. There are measurements such as ROGUE, METEOR and BLEU, also human evaluation. Can synthetic data help?

Thirdly, there's the challenge of accountability and transparency. As AI systems become more complex and autonomous, understanding their decision-making processes becomes more difficult. Developing transparent AI systems that can explain their decisions in human-understandable terms is crucial. I see more open source models, especially on HuggingFace etc, but with all these models it's difficult to discern how usable these models would be in the real world.