Source: it.toolbox.com
AI can unhitch new measures to make businesses more productive and generate new possibilities to enahnce the customer’s experience. That said, as with any new data-driven decision making tool, it can be a challenge to bring machine learning models into a business.
Machine learning models can distinguish intricate relationships between large numbers of data points. While this capability empowers AI models to reach unbelievable accuracy, examining the structure or weights of a model often tells users little about a model’s behavior. This means that for some decision-makers, particularly those in industries where confidence is critical, the advantages of AI can be out of reach without interpretability.
Therefore Google introduced its latest step in developing the interpretability of AI with Google Cloud AI Explanations. Explanations quantify each data factor’s contribution to the output of a machine learning model. These reports help companies know why the model made the decisions it did. Users can utilize this information to enhance models further or share valuable insights with the model’s consumers.
Of course, any explanation method has limitations. For one, AI Explanations reflect the patterns the model found in the data, but they don’t reveal any fundamental relationships in the data sample, population, or application.
Tracy Frey, Director of Strategy, Cloud AI, Google Cloud, explained, “We’re striving to make the most straightforward, useful explanation methods available to our customers while being transparent about its limitations. Explainable AI consists of tools and frameworks to deploy interpretable and inclusive machine learning models. AI Explanations for models hosted on AutoML Tables and Cloud AI Platform Predictions are available now.”
