Bias Analysis and Ethical Considerations in ML Models for Healthcare

The integration of Artificial Intelligence (AI) in the healthcare sector holds immense
potential to improve diagnostic accuracy and patient outcomes. However, the ethical
implications of AI systems, particularly regarding fairness and bias, are crucial concerns
that need to be addressed. In medical applications, biased models can exacerbate inequal-
ities, leading to disparities in diagnosis and treatment for underrepresented demographic
groups. Ensuring that AI systems are fair, transparent, and accountable is essential to
maintain trust and equity in healthcare.
This project focuses on evaluating and mitigating bias in ML models applied to the
Breast Cancer Dataset. It highlights the importance of analyzing sensitive variables
such as Race, Marital Status, and Age to ensure equitable treatment outcomes. Privacy
concerns are also addressed through data anonymization, emphasizing the need to safe-guard sensitive patient information. By incorporating fairness tools like Fairlearn and
interpretability techniques, this work provides recommendations for ethical AI implementation.