Mitigate AI risks, increase AI interpretability, and deploy AI with confidence using Explainable Artificial Intelligence (XAI) tools and frameworks
AI/ML models are being deployed across enterprise domains for prediction and decision-making, and the responsibility for making these predictions and decisions has shifted from humans on to the machines. This can only be sustained if there is a degree of trust and confidence in the models. The need for explainability is imperative to understand the ‘why’ and ‘how’ of predictions and decisions made by your training models. But black box models and ML algorithms that haven’t been integrated with AI-based model interpretation solutions can hamper progress and delay value realization significantly.
Leverage our explainable AI accelerator for end-to-end exploratory data analysis of your training and scoring data sets, so you can lift the lid on your models and get a clearer understanding of their workings. As an AI solutions company, we help you derive intelligible insights from your models and verify the explanation through analysis. Reduce downtime during setup with the convenience of plug-and-play by feeding model objects as inputs and improve time-to-value.
Create or upload new model objects for interpretations and access the same via the project log. Upload both training and model sets to combine data into insights and analyze them to articulate your AI systems’ decisions.
Get insights into the percentage contribution of a target class or the feature to the prediction value with explainable AI. Set explainability from your training models at global or local levels to build trust and enable enterprise-wide adoption.
Make edits to the actual value and easily interpret how the predicted value changes using simulation on your models to bring your processes out of the dark with our explainable AI solutions. Access scatter plot data distribution and make edits from any point without affecting the predicted value.
Leverage our data sample with our explainable AI solutions view that shows you various descriptions, from the correlation matrix to data feature information, so you can understand your insights from all angles for better impact. Get insights into the degree of dependency between variables and data statistics. Distribute data using bivariate analysis of continuous variables or continuous vs. categorical variables, class balance, and variable distribution.
Leverage outlier analysis of continuous variables to get a univariate view into your data set to remove abnormal and inaccurate observations and draw stronger conclusions. Our boxplot chart includes lower limit, Q1, median, Q3, and upper limit for ease of use.
Visualize model behavior through counterfactual insights from model probing
Test model performance between defined target classes and within the local scope
