User Driven

User-driven approaches in various scientific domains prioritize user interaction and control in model building, analysis, and interpretation. Current research focuses on improving the comprehensibility and predictability of machine learning models, particularly through enhanced visualization tools and the incorporation of user feedback in model development and evaluation, including the use of techniques like counterfactual explanations. This emphasis on user agency is crucial for building trust, ensuring responsible AI development, and facilitating the effective application of complex models across diverse fields, such as healthcare and social media regulation.