IndicFairFace: 用于审计和缓解视觉-语言模型中地理偏见的平衡印度面部数据集
IndicFairFace: Balanced Indian Face Dataset for Auditing and Mitigating Geographical Bias in Vision-Language Models
AI总结：
IndicFairFace是一个平衡的印度面部数据集，用于审计和缓解视觉-语言模型中的地理偏见，通过去偏方法减少偏见并保持检索准确性。
摘要：
视觉-语言模型（VLMs）已知会从其大规模网络训练数据中继承并放大社会偏见，其中印度人尤其被误表征。现有的公平意识数据集在改善全球种族和性别群体的民主平衡方面显著改进，但它们仍然将印度视为一个单一的单一类别。这种过度简化忽视了印度28个州和8个联合领土之间巨大的内部多样性，导致代表性和地理偏见。为了解决这一限制，我们提出了IndicFairFace，一个新颖且平衡的面部数据集，包含14,400张图像，代表印度的地理多样性。图像伦理地从维基媒体公共领域和开放许可的网络存储库中获取，并在州和性别之间均匀平衡。利用IndicFairFace，我们量化了在 prominent CLIP-based VLMs 中的国内地理偏见，并使用后处理迭代空空间投影去偏方法减少它。我们还表明，所采用的去偏方法不会对现有的嵌入空间产生负面影响，因为平均检索准确率在基准数据集上的下降小于1.5个百分点。我们的工作使IndicFairFace成为首个用于研究印度背景下VLMs地理偏见的基准。
英文摘要：
Vision-Language Models (VLMs) are known to inherit and amplify societal biases from their web-scale training data with Indian being particularly misrepresented. Existing fairness-aware datasets have significantly improved demographic balance across global race and gender groups, yet they continue to treat Indian as a single monolithic category. The oversimplification ignores the vast intra-national diversity across 28 states and 8 Union Territories of India and leads to representational and geographical bias. To address the limitation, we present IndicFairFace, a novel and balanced face dataset comprising 14,400 images representing geographical diversity of India. Images were sourced ethically from Wikimedia Commons and open-license web repositories and uniformly balanced across states and gender. Using IndicFairFace, we quantify intra-national geographical bias in prominent CLIP-based VLMs and reduce it using post-hoc Iterative Nullspace Projection debiasing approach. We also show that the adopted debiasing approach does not adversely impact the existing embedding space as the average drop in retrieval accuracy on benchmark datasets is less than 1.5 percent. Our work establishes IndicFairFace as the first benchmark to study geographical bias in VLMs for the Indian context.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
