According to a poll by Gartner, 45% of business executives claimed that their investments in artificial intelligence (AI) have increased driven by ChatGPT’s popularity. In recent years, Artificial Intelligence (AI) has experienced a remarkable surge, revolutionizing various industries and aspects of our daily lives. From virtual assistants to predictive analytics, AI applications have shown tremendous potential.
As AI systems become increasingly prevalent, understanding their decision-making processes has become a pressing concern. The concept of white-box AI, which emphasizes interpretability and transparency, has emerged as a response to this challenge.
In this blog post, we will explore white-box machine learning models, the importance of interpretability, and the methods employed to achieve transparency.
In a traditional AI system, also known as black-box AI, the model processes input data and provides an output without revealing the intermediate steps or decision-making process. In contrast, white-box AI aims to make the decision-making process transparent and interpretable, providing human-readable explanations for its outputs.
Several methods have been developed to create white-box AI models. Some include:
As the Artificial Intelligence (AI) landscape continues to evolve, the quest for transparency and interpretability becomes increasingly crucial. White-box AI, with its focus on providing insights into the decision-making process, holds the potential to revolutionize how we interact with AI systems.
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