Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification however there has been little exploration of the field for survival analysis. Survival analysis is the prediction task in which one attempts to predict the probability of an event occurring over time. Survival predictions are particularly important in sensitive settings such as when utilising machine learning for diagnosis and prognosis of patients. In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics. We explore this in an empirical experiment with 29 survival datasets and 8 measures. We find that measures of discrimination are able to capture bias well whereas there is less clarity with measures of calibration and scoring rules. We suggest further areas for research including prediction-based fairness metrics for distribution predictions.
翻译:测算公平是一个日益重要的领域,涉及发现和减轻机器学习模式中的偏见,在回归和分类方面有大量的文献,但很少探索生存分析领域; 生存分析是一项预测任务,即试图预测一段时间内发生事件的概率; 生存预测在敏感环境中特别重要,例如在利用机器学习诊断和预测病人时。 本文探讨了如何利用现有的生存指标衡量群体公平指标的偏向。 我们在一项实验中探索了这一点,有29个生存数据集和8项措施。我们发现,歧视措施能够很好地捕捉偏见,而校准和评分规则措施则不够明确。我们建议进一步的研究领域,包括基于预测的公平指标,用于分配预测。