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Title: Fairness-Aware Instrumentation of Preprocessing~Pipelines for Machine Learning
Surfacing and mitigating bias in ML pipelines is a complex topic, with a dire need to provide system-level support to data scientists. Humans should be empowered to debug these pipelines, in order to control for bias and to improve data quality and representativeness. We propose fairDAGs, an open-source library that extracts directed acyclic graph (DAG) representations of the data flow in preprocessing pipelines for ML. The library subsequently instruments the pipelines with tracing and visualization code to capture changes in data distributions and identify distortions with respect to protected group membership as the data travels through the pipeline. We illustrate the utility of fairDAGs, with experiments on publicly available ML pipelines.  more » « less
Award ID(s):
1926250 1934464
PAR ID:
10182459
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Workshop on Human-In-the-Loop Data Analytics (HILDA'20)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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