如何评估并缓解 AI 系统对不同群体的偏见？

什么是大模型幻觉？ 什么是大模型幻觉？包括复读机问题（重复生成内容）和胡说八道的原因。 如何减轻幻觉问题？具体方法包括：RLHF、SFT后出现幻觉的可能原因及处理、后训练减少脱离人设的幻觉、MCP工具较多时避免幻觉、LMM中解决图中没有但模型乱说的情况、通过算法规避虚假图文和隐私泄露、对抗性测试/红队攻击发现安全漏洞、后训练在安全合规中的作用。

• 偏见问题如何解决？

• 【模型泛化与人群差异】你有想做不同人种（比如黄种人白种人肯定不一样要区分开来的），不同地域的嘛？ 「字节跳动」

• 【价格歧视规避】怎么避免“杀熟”投诉？ 「美团」

• How do you evaluate bias in AI model outputs?

• Your model passes one fairness metric but fails another. How do you handle conflicting audit results?

• Your model was fair at deployment, but became biased 6 months later. How do you monitor continuously?

• How do you detect and mitigate bias in AI systems?

• Your resume screening AI rejects more female candidates for engineering roles. How do you fix gender bias?

• Your AI model passes bias checks by gender and race separately, but fails for intersectional groups. How do you handle it?