10. Building Trust in AI: The Crucial Role of Human Centered Machine Learning in Safety Critical Domains

The integration of machine learning (ML) models in safety-critical domains like aviation, healthcare etc., not only present significant opportunities but also pose serious challenges. ML /AI models implemented in the safety critical domain are known to improve performance and decision-making, but they also pose unacceptable dangers in various applications.

Firstly, the presentation focuses on the need for interpretable ML models emphasizing on the reliability, safety and trustworthiness.

Secondly presentation walks you through the success of Human in loop interpretable machine learning models and their role in bridging gap between domain experts and ML. In order to achieve model transparency, the presentation will go over the techniques like feature importance analysis, visualization tools and model-agnostics methods. Examples of these techniques are LIME and SHAP. Talk will also discuss the tactics for integrating the overcoming scalability challenges and human feedback loops.

Overall, the objectives of the Lightning Talk are to deliver a thorough understanding of impact and real-time application of human-in-the-loop interpretable ML models in safety-critical environments.

Explainable AI

Interpretable Machine Learning



Safety Critical Domain

Explainable AI 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025