What is Explainable AI (XAI) and Why You Should Care
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As technology continues to evolve and business executives look for the best way to keep their operations running smoothly, it’s important that they stay informed on the latest trends in data-driven technologies.
Chief among them is explainable AI (XAI), which has the potential to revolutionize how companies process and interpret large amounts of data in order to drive insightful decisions. We are living in a data-driven world and AI is at the helm of it. But with great power comes great responsibility, which is why XAI is a must-have for any business looking to succeed moving forward.
What is Explainable AI?
Artificial intelligence is becoming more complex and increasingly implemented across society, which makes explainability even more crucial.
IBM provides a simple but effective definition for XAI:
”Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms”
XAI helps describe an AI model, its expected impact and potential biases. All of this leads to better model accuracy, fairness, transparency and outcomes when AI is used for data-driven decision making.
Explainability is critical as AI algorithms take control of more applications and sectors, which brings along the risk of bias, faulty algorithms, and various other issues. By ensuring transparency for your company through explainability, you can truly leverage the power of AI.
Explainable AI is not just one single tool but rather a set of tools and frameworks that help you, your company and the public understand and interpret predictions made by machine learning models.
Solving the “Black Box” Effect with XAI
In contrast to explainable AI models, many of today’s AI systems are so advanced that humans must carry out a calculation process to retrace how the algorithm arrived at its result. This process leads to the “black…
