发现单个神经元就能像旋钮一样控制LLM的投资偏见，精准干预AI决策倾向。
arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows…
AI推荐医生会偏向谁？算法审计揭示大模型中的声誉与人口信号偏差。
arXiv:2608.14399v1 Announce Type: cross Abstract: Patients increasingly ask large language model (LLM) assistants which doctor to see, making these sy…
一篇论文揭示如何追踪LLM推理环节与偏见输出的因果链条，给AI可解释性提供新工具
arXiv:2608.14161v1 Announce Type: new Abstract: LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in …
提问方式竟能左右大模型性别偏见，揭秘语言习惯背后的隐性歧视，AI公平性研究者必读
arXiv:2608.13328v1 Announce Type: cross Abstract: Professional communication is increasingly mediated by LLMs - but do these models serve all users eq…
打破AI公平性评测的“应试教育”，用真实行为模式替代标准化测试分数。
arXiv:2605.12530v2 Announce Type: replace-cross Abstract: LLM fairness should be evaluated through in-situ behavioral pattern rather than standardized…
多智能体拆分分诊决策，能否在审计受限时揪出偏见？模拟研究揭示真实部署中的隐藏风险。
arXiv:2608.06949v1 Announce Type: new Abstract: Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-dea…
AI与人类招聘官协作效率究竟如何？这项研究用数据量化了人机协同的招聘收益，为HR决策提供实证依据。
arXiv:2507.08029v2 Announce Type: replace Abstract: Hiring algorithms have mostly scored the materials recruiters already see. Large language models (…
系统评估大模型去偏见技术能否真正提升下游决策公平性，结果或有颠覆性发现。
arXiv:2509.16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, w…
用图思维链改进因果发现，并揭示事后路径公平审计的脆弱性，适合关注AI公平与因果推理的研究者。
arXiv:2608.02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clini…
对比人与大模型对非母语日语的评价差异，揭示AI评估中的语言偏见风险。
arXiv:2608.01629v1 Announce Type: new Abstract: Large language models (LLMs) increasingly evaluate human writing in high-stakes domains such as hiring…
用多模型交叉审计替代提示工程去偏，为LLM裁判公正性提供新思路。
arXiv:2607.28636v1 Announce Type: new Abstract: LLMs increasingly serve as automated judges, but their judgments remain vulnerable to cognitive biases…
公平分配至关重要，这个新基准专测大模型在稀缺资源分配中的种族与性别偏见
arXiv:2607.28934v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly involved in the distribution of scarce resources, rais…
硅谷嘴上追捧年轻创始人，实际操作却充满矛盾？看20岁以下创业者如何应对年龄偏见与投资人的双重标准。
AI tools have democratized the opportunity to build, shortening the timelines of success and enabling more young people to start successful companies …
前沿视觉语言模型竟无图编造诊断，且编造内容与患者身份相关，揭示AI偏见风险。
arXiv:2607.26886v1 Announce Type: cross Abstract: When asked to describe a medical image that was never attached, frontier vision-language models do n…
新研究揭示LLM聊天AI对智力障碍人士的隐式偏见，推动AI伦理与公平性讨论
arXiv:2607.26062v1 Announce Type: cross Abstract: Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based…
AI检测工具对非母语写作者存在高达3.2%的误报率，教育领域依赖技术检测的隐患值得警惕。
Article URL: https://forta.com/blog/ai-in-the-classroom-part-2-the-fears-real-and-imagined Comments URL: https://news.ycombinator.com/item?id=49089268…
AI并非万能，HN热帖盘点那些难以被AI渗透的行业领域。
I anticipate a raft of answers along the lines of "AI + robots will automate everything eventually", but am more interested in areas where you think t…
深入剖析八种主流大模型的内在伦理逻辑，揭示AI决策中的价值观差异与潜在偏见
arXiv:2501.08951v2 Announce Type: replace Abstract: This study examines the expressed ethical logic of eight prominent large language models from Open…
LLM能去偏新闻标题以改善跨党派信任，但研究发现模型高估自身效果，结果发人深省。
arXiv:2605.01006v3 Announce Type: replace Abstract: Partisan news media erode cross-partisan trust, but large language models (LLMs) offer the potenti…
量化压缩大模型后暗藏偏见风险，这篇论文提出全新基准测试方法，为AI安全与公平性敲响警钟。
arXiv:2607.21063v1 Announce Type: new Abstract: Almost every large language model that reaches a broad audience is quantized: trained in full precisio…