Fairness Issue

Fairness in artificial intelligence (AI) and data-driven systems is a critical research area focusing on mitigating biases that lead to discriminatory outcomes across different demographic groups. Current research investigates fairness across various applications, including medical imaging, federated learning, and automated testing, exploring sources of bias in data collection, model training, and deployment, and employing techniques like differential privacy and in-processing fairness mitigation methods to address these issues. This work is crucial for ensuring equitable access to resources and opportunities and for building trustworthy AI systems that avoid perpetuating or exacerbating societal inequalities. The development of tools like Fairlearn highlights the growing commitment to practical implementation and responsible AI development.