Algorithmic Decision Making

Algorithmic decision-making (ADM) focuses on developing and analyzing algorithms that automate decisions across various domains, aiming to improve efficiency and objectivity while mitigating bias. Current research emphasizes understanding and addressing fairness concerns, particularly through the development of explainable AI (XAI) techniques and the exploration of different fairness metrics, often within the context of specific model architectures like Markov decision processes and neural networks. The significance of this field lies in its potential to improve decision-making across sectors, from healthcare and criminal justice to resource allocation, but also in the critical need to ensure ethical and equitable outcomes by addressing inherent biases and promoting transparency.