Responsible AI: Managing Bias, Accuracy, and Explainability *Description:* As AI becomes increasingly ubiquitous, ensuring its responsible development and deployment is crucial. Join this session to explore the vital aspects of Responsible AI: managing bias, accuracy, and explainability. *Key Takeaways:* 1. Understanding AI bias: sources, consequences, and mitigation strategies 2. Techniques for improving model accuracy and reliability 3. Explainability methods: interpreting AI decisions and models 4. Best practices for implementing Responsible AI in real-world applications 5. Google's tools and resources for building Responsible AI solutions *Agenda:* 1. Introduction to Responsible AI (5 minutes) 2. Managing Bias in AI (15 minutes) - Data preprocessing and curation - Fairness metrics and evaluation - Debiasing techniques 3. Improving Model Accuracy (15 minutes) - Data quality and augmentation - Regularization techniques - Ensemble methods 4. Explainability in AI (15 minutes) - Model interpretability techniques - Feature attribution methods - Transparency and accountability 5. Implementing Responsible AI with Google Tools (10 minutes) - TensorFlow Explainability - Google Cloud AI Platform - Fairness and bias detection libraries 6. Conclusion and Q&A (5-10 minutes)