Artificial intelligence (AI) can be misleading. Either your mind goes to Hollywood and the robots are taking over the planet or you imagine futuristic shows like Star Trek where food is miraculously created upon request by a computerized box. In reality, it is neither – and maybe not anywhere in the neighborhood. This interesting topic came to us from The European Sting in their article, “The term AI overpromises. Let’s make machine learning work better for humans instead.”
Most organizations have little knowledge of how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields where AI and machine learning are being applied. Some even question if there is actually anything deserving of the term AI.
Building a machine learning system for a given task is rather easy and will become easier. But understanding it is a different thing altogether.
Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. Why is this important? Because the results can have an impact on data security or safety.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
