Responsible AI Framework for Your Enterprise AI Product
Responsible AI Framework for Your Enterprise AI Product
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 45m | 125 MB
Instructor: Alina Zhang
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 45m | 125 MB
Instructor: Alina Zhang
What if your AI makes the wrong decision? A biased loan denial, a self-driving car crash, or a privacy scandal? Incidents like these aren’t merely science fiction; they can cost companies millions of dollars, with future losses projected in the billions.
In this course, senior data scientist Alina Zhang guides you through the Five Rings of responsible AI—a practical framework you can use to ensure your AI products are ethical, secure, transparent, privacy conscious, and fair. Explore real-world AI failures and thought experiments like the paperclip maximizer and smile maximization. Along the way, gain critical insights and expert perspectives from leading AI pioneers on how to design AI that respects privacy, eliminates bias, enhances security, and promotes transparency and accountability.
Learning objectives
- Advance your AI career by mastering responsible AI strategies essential for enterprise AI success.
- Master the Five Rings of responsible AI to develop AI that is ethical, secure, explainable, privacy conscious, and fair.
- Analyze real-world AI incidents including Amazon’s biased recruiting tool, Meta’s data misuse, Stanford University’s flawed vaccine algorithm, Tesla’s adversarial attack research, biased criminal justice algorithms, and deepfake scams in cryptocurrency investments.
- Gain expert insights from AI pioneers such as Geoffrey Hinton, a Nobel Prize winner, and other industry leaders working on responsible AI development.
- Mitigate AI risks using cutting-edge techniques to apply adversarial training, secure coding, and continuous monitoring to protect AI systems.
- Enhance AI fairness and accountability using diverse datasets, balanced teams, and fairness metrics to minimize bias.
- Improve AI transparency with explainability tools and methods like LIME and SHAP to make AI decisions interpretable.
- Strengthen privacy protection by implementing differential privacy and federated learning to safeguard sensitive data.
Responsible AI Framework for Your Enterprise AI Product
