GAMMA FACET

Open-Source Library for AI Model Explainability

Artificial intelligence can predict outcomes, but its inner workings can be a mystery. GAMMA FACET, our open-source library, cracks complex AI algorithms, uncovering how variables contribute to a model’s reasoning—and how you can get better results.

Request Demo

Request Demo

About GAMMA FACET

Predictive AI is not a magic crystal ball. It’s based on science and has a long track record. But for many business leaders, the analogy does raise a point. Without understanding complex machine-learning (ML) algorithms, how can we truly trust the models—let alone base strategies upon them?

GAMMA FACET, an open-source library by BCG GAMMA, takes a unique approach, showing how relevant features interact to determine key outcomes. This helps data scientists avoid common misinterpretations of ML models. And it reveals the variables that matter most. By explaining AI, FACET informs—and improves—decision making for both data science and the business.

By analyzing patterns and making predictions, AI algorithms help advance everything from manufacturing efficiency to social impact. But as AI models become more complex, it is harder to understand how they arrive at their conclusions. That knowledge is crucial, for when you know what is driving a result, you know what to improve—and where to optimize—to get better results.

FACET delivers insights on the workings of AI in two ways:

Model Inspection

Applying a unique geometric interpretation of ML models, FACET assesses how the variables interact; this transforms the explainability of ML models, as variables often act in a redundant or synergistic way. Understanding these dependencies has been difficult, leading to distorted interpretations of ML models. By uncovering these relationships, FACET reveals the true contributions of each variable.

Model Simulation

Once you know the critical variables and how they’re connected, you’re ready to optimize them. FACET’s simulation algorithms let data scientists run what-if scenarios—virtual experiments to discover how changing key parameters affects predicted outcomes. By helping users identify the parameters that matter most, FACET provides a roadmap for achieving even better outcomes.

Model Inspection

Model Simulation

Benefits

The Benefits of Understanding Complex ML Models

We designed FACET for easy use by data scientists. But the benefits extend across an organization. Chief among them:

Trust in AI

By opening the black box of AI algorithms, FACET brings a new level of transparency. It adds more nuance and context to model explanations by looking at all the elements as an interconnected whole. When business leaders understand what’s behind a prediction, they’re likely to feel confident aligning their strategy, efforts, and resources with it. They’re also more likely to extend the reach of AI.
Focused Efforts

By clarifying the roles that key parameters play, and testing what-if scenarios, FACET points the way to better results. Whether the goal is to cut costs, boost speed, improve yield, or better serve customers, patients, or constituents, organizations can focus their improvement efforts with surgical precision.
Responsible AI

As the saying goes, with great power comes great responsibility. And as AI algorithms grow increasingly complex, it’s critical for the business community to be vigilant in detecting—and eliminating—unfair bias. Through its interpretation of ML models, FACET fosters responsible AI and objective decision making.

Biopharma Industry

Maximizing production output of a pharmaceutical drug is a crucial goal, as it expands access to treatment while creating more value. The company used FACET’s explainable AI open-source library to identify parameters with the biggest impact on output, understand their relationships and dependencies, and conduct what-if simulations to find the optimal values for maximizing production volume.