TITLE: TRAPDOOR: Repurposing backdoors to detect dataset bias in machine learning-based genomic analysis 

ABSTRACT: Machine Learning (ML) has achieved unprecedented performance in several
applications including image, speech, text, and data analysis. Use of ML to
understand underlying patterns in gene mutations (genomics) has far-reaching
results, not only in overcoming diagnostic pitfalls, but also in designing
treatments for life-threatening diseases like cancer. Success and
sustainability of ML algorithms depends on the quality and diversity of data
collected and used for training. Under-representation of groups (ethnic groups,
gender groups, etc.) in such a dataset can lead to inaccurate predictions for
certain groups, which can further exacerbate systemic discrimination issues.
  In this work, we propose TRAPDOOR, a methodology for identification of biased
datasets by repurposing a technique that has been mostly proposed for nefarious
purposes: Neural network backdoors. We consider a typical collaborative
learning setting of the genomics supply chain, where data may come from
hospitals, collaborative projects, or research institutes to a central cloud
without awareness of bias against a sensitive group. In this context, we
develop a methodology to leak potential bias information of the collective data
without hampering the genuine performance using ML backdooring catered for
genomic applications. Using a real-world cancer dataset, we analyze the dataset
with the bias that already existed towards white individuals and also
introduced biases in datasets artificially, and our experimental result show
that TRAPDOOR can detect the presence of dataset bias with 100% accuracy, and
furthermore can also extract the extent of bias by recovering the percentage
with a small error.
Machine Learning (ML) has achieved unprecedented performance in several
applications including image, speech, text, and data analysis. Use of ML to
understand underlying patterns in gene mutations (genomics) has far-reaching
results, not only in overcoming diagnostic pitfalls, but also in designing
treatments for life-threatening diseases like cancer. Success and
sustainability of ML algorithms depends on the quality and diversity of data
collected and used for training. Under-representation of groups (ethnic groups,
gender groups, etc.) in such a dataset can lead to inaccurate predictions for
certain groups, which can further exacerbate systemic discrimination issues.
  In this work, we propose TRAPDOOR, a methodology for identification of biased
datasets by repurposing a technique that has been mostly proposed for nefarious
purposes: Neural network backdoors. We consider a typical collaborative
learning setting of the genomics supply chain, where data may come from
hospitals, collaborative projects, or research institutes to a central cloud
without awareness of bias against a sensitive group. In this context, we
develop a methodology to leak potential bias information of the collective data
without hampering the genuine performance using ML backdooring catered for
genomic applications. Using a real-world cancer dataset, we analyze the dataset
with the bias that already existed towards white individuals and also
introduced biases in datasets artificially, and our experimental result show
that TRAPDOOR can detect the presence of dataset bias with 100% accuracy, and
furthermore can also extract the extent of bias by recovering the percentage
with a small error.
TRAPDOOR: Repurposing backdoors to detect dataset bias in machine learning-based genomic analysis
http://arxiv.org/abs/2108.10132v2