  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.
