Effect of Demographic Bias on Skin Lesion Classification

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, quick

Summary

A study evaluates ResNet-based convolutional models for skin lesion classification, investigating demographic bias from patient sex and age in training data. Using linear programming, datasets with controlled demographic characteristics were generated to systematically assess bias effects across single-task, reinforcing multi-task, and adversarial learning strategies. Sex-based analysis showed sex-specific training optimizes performance, with male patient inclusion improving male subgroup results even in female-majority cases. Reinforcing and adversarial schemes reduced sex bias in balanced and female-majority datasets but were less effective in male-majority settings. Age-based analysis revealed performance declines with age, consistently favoring younger groups regardless of data distribution. Sex biases primarily stem from data imbalances, while age biases consistently favor younger groups, indicating distinct mitigation needs. Cross-dataset validation also highlighted that domain shifts significantly impact performance and bias patterns.

Key takeaway

For machine learning engineers developing skin lesion classifiers, you must recognize that sex and age biases have distinct origins. Address sex biases by ensuring balanced training data or employing multi-task/adversarial learning, especially in female-majority datasets. For age biases, anticipate declining performance in older patient groups and consider targeted strategies beyond data balancing, as younger groups are consistently favored. Your cross-dataset validation should also account for domain shifts impacting bias patterns.

Key insights

Demographic biases in skin lesion classification models stem from distinct mechanisms requiring targeted mitigation strategies.

Principles

• Sex-specific training optimizes model performance.
• Data imbalances primarily drive sex biases.
• Age biases consistently favor younger patient groups.

Method

Generate demographically controlled datasets via linear programming, then evaluate single-task, reinforcing multi-task, and adversarial learning strategies.

In practice

• Include male patients to improve male subgroup performance.
• Use reinforcing/adversarial learning for sex bias reduction.
• Anticipate performance decline in older age categories.

Topics

• Skin Lesion Classification
• Demographic Bias
• ResNet Models
• Multi-task Learning
• Adversarial Learning
• Data Imbalance
• Domain Shift

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.