The rise and understandability of AI systems have become serious topics in the AI tech sector as a result of AI’s rise. The demand for Explainable AI (XAI) has increased as these systems become more complicated and capable of making crucial judgments. This poses a critical question: Does XAI have the capacity to completely replace human positions, or does it primarily empower human experts?
Explainability in AI is an essential component that plays a significant and growing role in a variety of industry areas, including healthcare, finance, manufacturing, autonomous vehicles, and more, where their decisions have a direct impact on people’s lives. Uncertainty and mistrust are generated when an AI system makes decisions without explicitly stating how it arrived at them.
A gray area might result from a black box algorithm that is created to make judgments without revealing the reasons behind them, which can engender mistrust and reluctance. The “why” behind the AI’s decisions has left human specialists baffled by these models. For instance, a human healthcare provider may not understand the reasoning behind a diagnosis made by an AI model that saves a patient’s life. This lack of transparency can make specialists hesitant to accept the AI’s recommendation, which could cause delays in crucial decisions.
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No mention of Chain of Thought: https://arxiv.org/abs/2201.11903
It has been shown that an LLM can give significantly more accurate answers if it’s instructed to explain its thought process step by step with an example. The main problem is how to generate such examples. Easy to do for simple math problems, but incredibly difficult for medical diagnoses.
Not really explainable AI, because it cannot explain how it came up with its reasoning. It’s still a black box we cannot possibly understand. But there’s a possibility that models learned to explain its conclusions is the future.