Enhancing Fairness in Medical Diagnosis based on Deep Learning and Influence Score

Music City Center 

The prevalence of artificial intelligence (AI) has highlighted
significant issues, particularly bias in AI systems, which is a
serious concern in fields like medical diagnosis. Bias often arises
from data collection practices that focus on specific populations,
leading to models that exhibit discriminatory behavior and unequal
prediction performance across different groups. To address this, we
propose a novel feature selection method based on deep learning and
statistics, aimed at eliminating discriminatory effects while
preserving predictive performance. This method employs the influence
score (I-score) to account for interactions among multiple features,
allowing for the exclusion of biased features and enhancing model
fairness. We conducted empirical studies using the ISIC 2019 and ASAN
skin lesion datasets, demonstrating that our fair I-score model
effectively classifies skin lesion types by mitigating inherent
biases. Additionally, we introduced a fairness model architecture for
multi-label classification that does not rely on data collection or
pre-processing, addressing biases from multiple risk factors. By
integrating the influence score and the backward dropping algorithm,
we derived important influence features and proposed an operational
definition of fairness based on the area under the receiver operating
characteristic curve. Furthermore, we expanded the application of the
fair influence score model to scenarios with missing sensitive
features, utilizing various imputation methods to construct fairer
models. Our results indicate that, compared to the baseline model, our
approach shows improved fairness and predictive performance on
external validation datasets. Overall, these studies enhance the
fairness of medical diagnosis models and demonstrate that deep
learning can maintain strong predictive capabilities even in diverse
data and sensitive feature absence scenarios, providing valuable
insights for future AI fairness design. This report is based on the
study results with Professor Shaw-Hwa Lo in Columbia University, Dr.
Jacky Chung-Hao Wu, and other collaborators.

Artificial Intelligence (AI)

Deep Learning

Fairness

Medical Diagnosis

Influence Score (I-score)


Backward Dropping Algorithm