From loan decisions to recommending medical treatments, machine and deep learning models are making determinations that affect our lives. To trust artificial intelligence, we need to explain how AI makes recommendations. SAS is committed to solving the constantly evolving challenge of the explainability of AI. With natural language and open industry-standard frameworks directly embedded in the SAS Platform, we help surface general biases in data and models and provide clarity into the factors and variables that lead to a decision.
Want more specifics? Using natural language and open industry-standard frameworks like LIME, Partial Dependence, Individual Conditional Expectation, Kernel Shap and others, you can, with a single click, discover how even the most complex models came to a prediction. We can also help you reflect general biases in the data or model by automatically surfacing potential hidden relationships, enabling you to run what-if analyses.