From the course: MLOps Essentials: Model Deployment and Monitoring
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Elements of responsible AI
From the course: MLOps Essentials: Model Deployment and Monitoring
Elements of responsible AI
- [Narrator] Responsible AI is an area that is gaining more importance around the world as concerns about the harmful effects of AI grow in our society. While there is excitement about the disruptive potential of AI in improving our personal lives and business, there are also ethical concerns around bias, security, privacy, and transparency of these solutions. Responsible AI, or RAI for short, is a set of principles, processes, and practices to ensure that ML models are accepted in our society as ethical, thereby building trust in its capabilities. This trust is important for the wide acceptance of AI. Ensuring conformance to responsible AI principles and practices is a key area for MLOps. What are the key elements of responsible AI? To begin, ML models should be explainable. This means it should be possible to explain why a model made a specific prediction by looking at the inputs to the model. It helps to eliminate…
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