What is Responsible AI?
Responsible AI is a framework for building and deploying AI solutions in a way that is fair, transparent, accountable, and ethical. It's about proactively addressing potential harms and maximizing the positive impact of AI.
This includes considerations like bias detection and mitigation, explainability, privacy, and security.
- Fairness: Ensuring AI systems don't discriminate against certain groups.
- Transparency: Understanding how AI models make decisions.
- Accountability: Establishing clear lines of responsibility for AI systems.
- Ethics: Adhering to ethical principles in the design and deployment of AI.
Key Components of Responsible AI
Azure Machine Learning provides tools and services to help you build responsible AI solutions.
- Fairness Assessment: Identify and mitigate bias in your datasets and models.
- Explainable AI (XAI): Understand and interpret the reasoning behind your model's predictions.
- Model Monitoring: Continuously track your model's performance and identify any shifts in behavior that could indicate bias or inaccuracies.
- Data Governance: Implement policies and processes to ensure data quality and responsible data usage.
Resources
Explore further resources to learn more about Responsible AI in Azure Machine Learning.