The field of artificial intelligence is moving towards increased transparency and interpretability, with a focus on explainable AI and counterfactual reasoning. Recent developments have highlighted the importance of understanding how models make decisions and identifying potential biases. Counterfactual explanations have emerged as a powerful tool for providing insights into model behavior and suggesting targeted interventions to achieve favorable outcomes. Noteworthy papers in this area have introduced novel frameworks for model-agnostic counterfactual generation, causal constraint-based counterfactual reasoning, and explainable counterfactual reasoning for depression medication selection. Notable papers include: LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence, which presents a competitor-discovery AI agent that achieves 83% recall in identifying competing drug names. MC3G: Model Agnostic Causally Constrained Counterfactual Generation, which proposes a novel framework for generating counterfactuals that produce favorable outcomes for black-box models. P2C: Path to Counterfactuals, which introduces a model-agnostic framework that produces a plan for converting an unfavorable outcome to a causally consistent favorable outcome.