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Making AI Accountable, Ethical, and Responsible
Artificial Intelligence (AI)is disrupting nearly every industry, but only a few remain privy to its innermost workings. Run-on machine learning algorithms, AI models use logic that is often obscured from users and susceptible to error due to inherent fallacies within datasets, which is the primary fuel for AI engines. This point of view looks at the dangers of AI biases and how Explainable AI charts transparent and trustworthy paths from data to decisions.
In this point of view, you will learn about:
- AI biases and their impact
- What is AI explainability?
- Role of Explainable AI in resolving bias
- Keyways to eliminate bias in AI models