Explainable AI (XAI)

As Artificial Intelligence (AI) solutions become ever more ubiquitous, there is still a serious lack of understanding in how these systems make decisions. The brittle nature of the statistical correlations learned by these models are often overlooked. To address this gap, Explainable and Interpretable Artificial Intelligence (XAI) research seeks to explain how and why models make their decisions. Without this understanding, users of AI systems are left blindly trusting the output of their AI models. Especially in high-stakes decision-making such as with self-driving cars and criminal sentencing, XAI methods provide trust anchors in how these systems work in order to verify the validity of these decisions and debug them when they are not...

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Explainable AI (XAI)

As Artificial Intelligence (AI) solutions become ever more ubiquitous, there is still a serious lack of understanding in how these systems make decisions. The brittle nature of the statistical correlations learned by these models are often overlooked. To address this gap, Explainable and Interpretable Artificial Intelligence (XAI) research seeks to explain how and why models make their decisions. Without this understanding, users of AI systems are left blindly trusting the output of their AI models. Especially in high-stakes decision-making such as with self-driving cars and criminal sentencing, XAI methods provide trust anchors in how these systems work in order to verify the validity of these decisions and debug them when they are not...

Read more...Research