Social B(eye)as over Time (SBT) Dataset

Many eyes have scrutinized the social behaviors of computer vision services, given their popularity with researchers and developers. When analyzing images depicting people, their descriptions often reflect social inequalities and stereotypes, yet the proprietary nature of these services mean that it is difficult to anticipate or explain their behaviors. Mechanisms providing oversight of these processes can enable more responsible use, allowing stakeholders to audit their behaviors and track potential changes over time. Previously, in 2019, we audited image tagging algorithms for social bias when processing images of people. In this work, we i) present data from an audit of the same services three years later, with ii) additional outputs for input images depicting other racial/ethnic groups and iii) a toolkit enabling several fully-automated analyses on the algorithms' behaviors across time.鉴于计算机视觉服务在研究人员与开发者群体中广受青睐，多方主体已对其社会行为展开细致审视。在对包含人物的图像进行分析时，这类服务生成的描述结果往往会映射出社会不平等与刻板印象；但由于此类服务均为专有闭源系统，其行为逻辑难以被预判与阐释。建立针对此类流程的监督机制，可推动更负责任的应用落地，让各方利益相关者能够审计算法行为并实时追踪其潜在演化。早在2019年，我们便曾针对处理人物图像的图像标注算法开展社会偏见审计工作。本研究中，我们完成了三项核心工作：其一，时隔三年后对同款计算机视觉服务开展二次审计并公开相关数据；其二，新增针对描绘其他种族/族裔群体的输入图像的分析输出；其三，推出一款工具包，可针对算法随时间推移的行为开展多种全自动化分析。

搜集汇总

背景与挑战

背景概述

Social B(eye)as over Time (SBT) Dataset是一个用于审计计算机视觉图像标注算法社会偏见的数据集，重点关注算法在处理人物图像时反映的不平等和刻板印象。该数据集提供了2019年审计后三年的新数据，扩展了针对不同种族/族裔群体的图像输出，并包含一个工具包，支持对算法行为进行跨时间的全自动分析，旨在促进更负责任的人工智能使用。