Algorithmic Recourse

Algorithmic recourse aims to provide individuals with actionable steps to overturn unfavorable predictions from machine learning models, promoting fairness and transparency in automated decision-making. Current research focuses on generating robust and cost-effective recourse actions, often employing techniques like Markov Decision Processes, reinforcement learning, and optimization algorithms tailored to specific model architectures (e.g., decision trees, generalized additive models). This field is significant because it addresses the need for explainable AI and empowers individuals to challenge biased or inaccurate automated decisions, impacting areas like finance, healthcare, and criminal justice.