It’s almost impossible to ensure fairness in ML models if you don’t understand how models are behaving or why certain predictions are made. How can model bias be detected and assessed if you can’t extract causal drivers in your data and models?
Fiddler reduces model risk by enabling the deployment of AI governance and model risk management processes. Not only are coverage and efficiency increased, but human input into the decision-making loop for ML is enabled.
No one wants to manage a PR catastrophe or incur fines and penalties.
Fiddler provides practical tools that support internal model governance processes, and we provide practical tools, expert guidance, and white glove customer service to develop responsible AI practices. Fiddler integrates deep explainable AI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI.
How nice would it be to select multiple protected attributes at the same time to detect hidden intersectional unfairness? Or to benefit from fairness metrics when analyzing model performance?
With Fiddler, you can compare and measure a multitude of fairness metrics and evaluate, detect, and mitigate potential bias in both training and production datasets.
Detect algorithmic bias using powerful visualizations and metrics
Discover potential bias by examining multiple dimensions simultaneously (e.g. gender, race, etc.)
Obtain fairness information by comparing model outcomes and model performance for each subgroup of interest
Check for fairness in your dataset before training your model by catching feature dependencies and ensuring your labels are balanced across subgroups
Use out-of-the-box fairness metrics, such as disparate impact, demographic parity, equal opportunity, and group benefit, to help you increase transparency in your models