AI Trust Index
CORTEX CERTIFAI
Measure Risk and Bias

Build Trust Into Your AI Applications

Cortex Certifai provides explainability, fairness, robustness, and compliance to your applications—the anchors of trustworthy Artificial Intelligence.

Why Cortex Certifai?

Explainability and Governance

Certfai answers four key questions to build trust and mitigate risk and bias (and to maintain compliance within regulated industries).

Four Key Indicators

How did the AI and model arrive at its predictions?
Has the model been unfair to a particular group?
Is the model compliant with industry regulations?
How easily can the model be fooled?

Certifai Trust index

Eliminate the Guesswork to Understand Risk

Certifai automates Data and Model vulnerability detection and creates black-box transparency. It explains decisions from predictive models, uncovers bias and finds weaknesses in the data.

The Six Key Elements of Trust

Certifai generates a numeric score based on 6 key elements of trust: fairness / bias, robustness, explainability, accuracy, compliance, and data quality. Our AI Trust Index allows you to compare your model to industry benchmarks.

Role-based Reporting

Engage key stakeholders in building trusted AI with role-based reporting.

Data Science Teams and IT Experts
Product and Marketing Executives
Customers and Employees
Compliance and Risk Executives

The Components of the AI Trust Index

Effectiveness

In order to trust the predictions made by an algorithm, we must be able to measure the effectiveness of the predictions in solving the business problem.

Bias & Fairness

In Trusted AI systems the data and models represent the real world and are free of algorithmic biases. This mitigates skewed decision making and reasoning that results in errors and unintended consequences.

Explainability

To build trust for human users, transparency is a must. AI systems built around this principle explain the decision process and eliminate the black-box concerns for stakeholders.

Robustness

As with other technologies, cyber-attacks can penetrate and fool AI systems. Trusted AI systems detect and provide protection against attacks and understand how issues with data quality impact system performance.

Data Quality

Data is the fuel that powers AI. Trusted AI systems ensure visibility concerning data drifts, data poisoning, and data validity and fit. They also ensure legal justifications to use and process the data.

Compliance

Trusted AI systems take a holistic design, implementation, and governance model that ensures that AI systems operate within the boundaries of local, national and industry regulation. They are built and controlled in a compliant and auditable manner.