Fairness Notion

Fairness notions in machine learning aim to mitigate biases in algorithms that disproportionately affect certain groups, focusing on ensuring equitable outcomes across diverse populations. Current research explores various fairness definitions (e.g., statistical parity, equalized odds, counterfactual fairness), developing and comparing methods like adversarial debiasing, data augmentation, and post-processing techniques across different model architectures (including deep learning and reinforcement learning). This work is crucial for building trustworthy and ethical AI systems, impacting fields like healthcare, finance, and criminal justice by promoting fairness and reducing discriminatory outcomes.