Biased Decision

Biased decision-making in artificial intelligence and related systems is a significant research area focused on identifying and mitigating unfair or inaccurate outcomes stemming from biased data or algorithms. Current research emphasizes developing methods to detect and correct for bias, including techniques like counterfactual fairness, debiasing algorithms (e.g., using focal loss or adversarial learning), and fairness-aware model training. These efforts are crucial for ensuring the responsible development and deployment of AI systems across various applications, from loan approvals and hiring processes to medical diagnoses and social media algorithms, ultimately aiming to reduce societal inequalities and improve the reliability of AI-driven decisions.