How Explainable AI Enables Deeper Insights and Better Decision Making for IT
Currently, the two most dominant technologies in the world are machine learning (ML) and artificial intelligence (AI), as these aid numerous industries in resolving their business decisions. Therefore, to accelerate business-related decisions, IT professionals work on various business situations and develop data for AI and ML platforms.
The ML and AI platforms pick appropriate algorithms, provide answers based on predictions, and recommend solutions for your business; however, for the longest time, stakeholders have been worried about whether to trust AI and ML-based decisions, which has been a valid concern. Therefore, ML models are universally accepted as “black boxes,” as AI professionals could not once explain what happened to the data between the input and output.
However, the revolutionary concept of explainable AI (XAI) has transformed the way ML and AI engineering operate, making the process more convincing for stakeholders and AI professionals to implement these technologies into the business.
Why Is XAI Vital for AI Professionals?
Based on a report by Fair Isaac Corporation (FICO), more than 64% of IT professionals cannot explain how AI and ML models determine predictions and decision-making.
However, the Defense Advanced Research Project Agency (DARPA) resolved the queries of millions of AI professionals by developing “explainable AI” (XAI); the XAI explains the steps, from input to output, of the AI models, making the solutions more transparent and solving the problem of the black box.
Let's consider an example. It has been noted that conventional ML algorithms can sometimes produce different results, which can make it challenging for IT professionals to understand how the AI system works and arrive at a particular conclusion.
After understanding the XAI framework, IT professionals got a clear and concise explanation of the factors that contribute to a specific output, enabling them to make better decisions by providing more transparency and accuracy into the underlying data and processes driving the organization.
With XAI, AI professionals can deal with numerous techniques that help them choose the correct algorithms and functions in an AI and ML lifecycle and explain the model's outcome properly.
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