Large language models (LLMs) are powerful tools, but they can sometimes generate harmful content, reflecting biases or stereotypes present in their training data. Researchers are exploring ways to make these models "self-correct"—to identify and fix these issues on their own, without external guidance. A new study examines the effectiveness of this "moral self-correction." The research dives into *how* these self-correction instructions change the internal workings of LLMs. Interestingly, they found that while the models get better at avoiding biased or toxic language, the underlying "moral compass" in the model’s hidden states might not be truly changing. The study indicates that LLMs learn a shortcut—how to give the right answer—rather than actually unlearning the bias itself. Think of it like a student learning to pass a test by memorizing answers, but not really understanding the material. This “superficial” self-correction is effective for multiple-choice questions where the model can adjust its response based on which answers are more morally acceptable, but it struggles with more open-ended language generation tasks. In those cases, the LLM often simply adds morally neutral text to its original response, instead of addressing the harmful part directly. For instance, if asked to rewrite a toxic sentence, the LLM might keep the toxic part but add on something like "...but that’s not okay.” This behavior, while showing a certain level of learning, reveals a deeper challenge: Can AI truly understand and fix its ethical shortcomings, or are we just teaching it to cover them up? Future research needs to explore how we can move beyond superficial self-correction to create AI systems that truly understand right and wrong. This raises important questions about how to best integrate external feedback and make these models less reliant on these shortcuts, ultimately leading to truly ethical AI.