Shining a Light Into AI’s Black Boxes
As artificial intelligence systems are increasingly deployed in the utility and damage prevention sectors, a major challenge looms: the opacity and lack of transparency in many AI models used for these tasks. The “black box” nature of complex machine learning algorithms like deep neural networks makes it extremely difficult to understand the reasoning and decision-making process behind their outputs. This poses a serious risk when the AI system’s decisions could lead to service disruptions, equipment damage, or even public safety hazards. In order to build trust with the technology, we need users to understand how it is making decisions, what the limits are and what the inputs are.
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As artificial intelligence systems are increasingly deployed in the utility and damage prevention sectors, a major challenge looms: the opacity and lack of transparency in many AI models used for these tasks. The “black box” nature of complex machine learning algorithms like deep neural networks makes it extremely difficult to understand the reasoning and decision-making process behind their outputs. This poses a serious risk when the AI system’s decisions could lead to service disruptions, equipment damage, or even public safety hazards. In order to build trust with the technology, we need users to understand how it is making decisions, what the limits are and what the inputs are.
