+12During your learning journey about Generative AI, you might have encountered the phrase "Explainable AI."
Have you encountered this term, and do you have any understanding of its meaning and significance?
We would like to hear your thoughts, please let us know in the comments section.
+12As we haven't heard back from others, let me share my views about Explainable AI.
"Explainable AI" (XAI) refers to the capability of artificial intelligence systems to provide clear and comprehensible explanations for their decisions and actions. This means that AI models can clarify why they made a specific prediction or choice in a way that humans can easily understand.
XAI is vital because it fosters trust and accountability in AI systems. It allows users and the general public to have confidence in AI decisions, aids in bias detection and mitigation, ensures compliance with regulations, facilitates debugging and improvement of models, enhances user understanding, and addresses ethical concerns.
Overall, XAI plays a pivotal role in making AI more transparent, ethical, and user-friendly.
+12As we haven't heard back from others, let me share my views about Explainable AI.
"Explainable AI" (XAI) refers to the capability of artificial intelligence systems to provide clear and comprehensible explanations for their decisions and actions. This means that AI models can clarify why they made a specific prediction or choice in a way that humans can easily understand.
XAI is vital because it fosters trust and accountability in AI systems. It allows users and the general public to have confidence in AI decisions, aids in bias detection and mitigation, ensures compliance with regulations, facilitates debugging and improvement of models, enhances user understanding, and addresses ethical concerns.
Overall, XAI plays a pivotal role in making AI more transparent, ethical, and user-friendly.
In the context of ChatGPT and other LLMs, I heard of a small study which showed that asking a model to explain its answers step-by-step can increase response accuracy. The model is forced to “think” critically about each of the intervening steps. It forces the model to consider the larger question in more detail and removes some of the room for hallucinations.
I heard it explained in the context of math, but I’m sure it applies across the board.
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