I saw @Mira_Network discussing a very realistic issue, which is that everyone is currently very concerned about the AI "hallucination" problem. After all, hallucinations can be partially resolved through technical means. For example, using knowledge graphs, retrieval enhancement, and strictly selecting data can reduce the occurrence of false information to some extent. But bias is not that simple. Even if you are careful in selecting training data, there will still be potential biases within it. Moreover, the human reviewers themselves carry personal viewpoints and blind spots, which are difficult to avoid. What’s more troublesome is that these AI systems are often concentrated in the hands of a few large companies, and the values of the developers can also influence the results, making it impossible to achieve true neutrality. This leads to an awkward situation: the more powerful and centralized AI becomes, the harder it is to eliminate the influence of bias. Even the tools used to discover and correct biases are also likely to fall into the trap of bias. In other words, the very things we use to solve problems may themselves be the source of the problems. I think this reminds us that the development of AI is not only a technical challenge but also a social and ethical issue. To truly solve bias, relying solely on technology is not enough; we need to involve more diverse voices, making AI more open and transparent to avoid unnecessary detours. The key is that there is a leaderboard activity on @KaitoAI, and the project on @arbitrum is also very popular, so you might want to check out $ENERGY, $SHOUT, $Bane, $Chao, $BOOM, and $666 on @shoutdotfun.