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Responsible AI and explainable AI (XAI) have emerged as crucial pillars in building ethical and trustworthy systems. This talk delves into the journey from responsible AI to XAI, exploring how these concepts intersect showcasing use cases and exploring open source frameworks.
RAI emphasizes the importance of fairness, transparency, accountability, and privacy in AI development. It addresses the risks associated with unchecked AI systems and promotes ethical practices. However, the challenge lies in interpreting and understanding the inner workings of complex AI models. This is where XAI comes into play. XAI techniques provide a means to unravel the "black box" of AI decision-making, enabling better understanding and interpretability. By incorporating XAI, we can uncover biases, improve fairness, enhance accountability, and foster trust in AI systems. This talk will also explore a recent approach related with human-focused XAI. Join me to explore the synergistic relationship between responsible AI and XAI, and unravel the path towards transparent, interpretable, and trustworthy AI systems.
Passionate about helping people discover the exciting world of artificial intelligence, Ezequiel is a frequent AI conference presenter and the creator of use cases, tutorials, and guides that help developers adopt open source AI tools.
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