Machine learning

We propose a simulation-based framework called FairSense to detect and analyze long-term unfairness in ML-enabled systems.

We show that GitHub Copilot can generate code with the symptoms of SATD, both prompted and unprompted. Moreover, we demonstrate the tool’s ability to automatically repay SATD under different circumstances and qualitatively investigate the characteristics of successful and unsuccessful comments.

We proposed Fairify, an approach to make individual fairness verification tractable for the developers. The key idea is that many neurons in the NN always remain inactive when a smaller part of the input domain is considered. So, Fairify leverages white-box access to the models in production and then apply formal analysis based pruning.

We comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design.

Our approach includes two key innovations: a novel optimization function and a fairness-aware search space. By improving the default optimization function of AutoML and incorporating fairness objectives, we are able to mitigate bias with little to no loss of accuracy. Additionally, we propose a fairness-aware search space pruning method for AutoML to reduce computational cost and repair time.

We provided a comprehensive taxonomy of machine learning SATDs. Our study analyzes ML SATD type organizations, their frequencies within stages of ML software, the differences between ML SATDs in applications and tools, and the effort of ML SATD removals. The findings discovered suggest implications for ML developers and researchers to create maintainable ML systems.

We introduced the causal method of fairness to reason about the fairness impact of data preprocessing stages in ML pipeline. We leveraged existing metrics to define the fairness measures of the stages. Then we conducted a detailed fairness evaluation of the preprocessing stages in 37 pipelines collected from three different sources.

We proposed causal reasoning in machine learning pipeline to measure fairness of data preprocessing stages.

We have focused on the empirical evaluation of fairness and mitigations on real-world machine learning models. We have created a benchmark of 40 top-rated models from Kaggle used for 5 different tasks, and then using a comprehensive set of fairness metrics, evaluated their fairness. Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance.

D4 Institute is an interdisciplinary data science hub at Iowa State university where professors, graduate students, REU students, and researchers from Computer Science, Electrical Engineering, Mathematics, Statistics collaborate to ensure the dependability of data science.