Machine Learning algorithms (ML) impact virtually every aspect of human lives
and have found use across diverse sectors, including healthcare, finance, and
education. Often, ML algorithms have been found to exacerbate societal biases
presented in datasets, leading to adversarial impacts on subsets/groups of
individuals, in many cases minority groups. To effectively mitigate these
untoward effects, it is crucial that disparities/biases are identified and
assessed early in a ML pipeline. This proactive approach facilitates timely
interventions to prevent bias amplification and reduce complexity at later
stages of model development. In this paper, we introduce DispaRisk, a novel
framework designed to proactively assess the potential risks of disparities in
datasets during the initial stages of the ML pipeline. We evaluate DispaRisk's
effectiveness by benchmarking it with commonly used datasets in fairness
research. Our findings demonstrate the capabilities of DispaRisk to identify
datasets with a high-risk of discrimination, model families prone to biases,
and characteristics that heighten discrimination susceptibility in a ML
pipeline. The code for our experiments is available in the following
repository: this https URL

介绍 DispaRisk，这是一种新颖的框架，旨在在 ML 流程的初期阶段，主动评估数据集中差异的潜在风险。通过与公平研究中常用的数据集进行基准测试，我们发现 DispaRisk 能够识别具有高歧视风险、易受偏见影响的模型家族以及增加 ML 流程中歧视敏感性的特征。