This thesis explores open-sourced machine learning (ML) model explanation tools to understand whether these tools can allow a layman to visualize, understand, and suggest intuitive remedies to unfairness in ML-based decision-support systems. Machine learning models trained on datasets biased against minority groups are increasingly used to guide life-altering social decisions, prompting the urgent need to study their logic for unfairness. Due to this problem's impact on vast populations of the general public, it is critical for the layperson -- not just subject matter experts in social justice or machine learning experts -- to understand the nature of unfairness within these algorithms and the potential trade-offs. Existing research on fairness in machine learning focuses mostly on the mathematical definitions and tools to understand and remedy unfair models, with some directly citing user-interactive tools as necessary for future work. This thesis presents FairLay-ML, a proof-of-concept GUI integrating some of the most promising tools to provide intuitive explanations for unfair logic in ML models by integrating existing research tools (e.g. Local Interpretable Model-Agnostic Explanations) with existing ML-focused GUI (e.g. Python Streamlit). We test FairLay-ML using models of various accuracy and fairness generated by an unfairness detector tool, Parfait-ML, and validate our results using Themis. Our study finds that the technology stack used for FairLay-ML makes it easy to install and provides real-time black-box explanations of pre-trained models to users. Furthermore, the explanations provided translate to actionable remedies.

翻译：本论文探讨开源机器学习模型解释工具，旨在理解这些工具是否能让非专业人士可视化、理解并建议基于机器学习的决策支持系统中不公平问题的直观补救措施。在针对少数群体存在偏倚的数据集上训练的机器学习模型，正日益被用于指导改变人生的社会决策，这迫切需要我们研究其逻辑中是否存在不公平性。鉴于该问题对广大公众群体的影响，不仅社会正义领域专家或机器学习专家，普通人也必须理解这些算法中不公平性的本质及其潜在权衡。现有机器学习公平性研究主要集中于数学定义及理解与补救不公平模型的工具，其中部分研究直接指出用户交互式工具是未来工作的必要方向。本文提出FairLay-ML，一个概念验证型图形用户界面，通过整合现有研究工具（例如局部可解释模型无关解释）与现有机器学习图形用户界面（例如Python Streamlit），为机器学习模型中的不公平逻辑提供直观解释。我们使用由不公平性检测工具Parfait-ML生成的不同准确率和公平性水平的模型测试FairLay-ML，并通过Themis验证结果。研究发现，FairLay-ML所采用的技术栈易于安装，并能向用户提供预训练模型的实时黑箱解释。此外，所提供的解释可转化为可操作的补救措施。