However, it's worth noting that GPT models do not process details with Theory of Mind (ToM) in the same way as creatures in nature do. When people think about others, there are electrical activities occurring between neurons within the nervous system. This results from information processing and transmission, phenomena that can be explained by neuroscience. In contrast, GPT models, and artificial intelligence models in general, do not exhibit similar neural interactivity. Instead, they generate responses through statistical training and testing based on the algorithms and information provided to them. This shows that these models do not fully autonomously manage ToM in their programming. Instead, they offer ToM feedback in accordance with different contexts in the environment. 

          This dependency on ToM raises significant ethical questions: Is it ethical to present the results of ToM feedback without understanding the origins of the training sets? This is similar to ethical issues surrounding the application of machine learning models in real life. AI models that generate responses containing ToM elements may also contain biases from the training datasets provided by the designers for the algorithms. These datasets may carry historical biases, selection biases, or even statistical biases that result from uncleaned data. The biases present in reflections provided by AI models, to varying degrees, correspond to the degree of biases and discrimination in the dataset. These biases could be amplified through the process of feedback loops. To make AI model responses more impartial and reasonable, and to align AI ethical principles with those of modern human society, scientists will need to address issues regarding data privacy, data transparency, and information justice.