Cortex Certifai generates the first-ever composite trust score, the AI Trust Index, that measures data and model risks related to Performance, Data Quality, Robustness, Explainability, Fairness, and Compliance. Certifai can be applied to any black-box model including machine learning models, statistical models, business rules, and other predictive models and works with a variety of input data.
In order to trust the predictions made by an algorithm, we must be able to measure the effectiveness of the predictions in solving the target business problem
Bias & Fairness
Trusted AI systems ensure that the data and models being used is representative of the real world and the AI models are free of algorithmic biases to mitigate skewed decision making and reasoning, resulting in reasoning errors and unintended consequences
AI systems built using Trusted AI principles and software understand stakeholder concerns for decision interpretability and provide business process, algorithmic, and operational transparency so human users will be able to understand and trust decisions
As with other technologies, cyber-attacks can penetrate and fool AI systems. Trusted AI systems provide ability to detect and provide protection against adversarial attacks while understanding how issues with data quality impact system performance.
Data is the fuel that powers an AI. AI systems built using Trusted AI principles will ensure user visibility around data drifts, data poisoning, and ensure data validity and fit while ensuring legal justifications to use and process the data.
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 and are built and controlled in a compliant and auditable manner.
Cortex Certifai helps businesses automate the management of AI Business Risk by answering pressing questions, such as:
- How did the AI and model predict what it predicted?
- Has the model been unfair to a particular group?
- Is the model compliant with industry regulations?
- How easily can the model be fooled?
To trust a digital system, we must have confidence in its decisions. However, AI systems learn and evolve over time, and contain many hidden decision processing layers, making auditability and explainability challenging.
Explainability, fairness, robustness, and compliance are the anchors of trustworthy AI. Leaders will soon be required to explain how their automated decision making systems are making inferences and decisions or risk diminished public trust, revenue losses, and regulatory backlash and fines.
Trust as a Service For Any Cloud Provider
Enterprises using the Red Hat OpenShift stack of AI Services, trust Certifai to automate algorithmic and data vulnerability and risk management
Detect and score black-box model and data risks to add trust-as-a-service to digital systems
Automates data and model vulnerability detection and scoring
Certifai takes the guesswork out of understanding risks introduced by automated decisions powered by predictive models, machine learning, and other technologies. Certifai automatically scans data and black-box models to learn how to explain decisions, uncover hidden bias, and probe for weaknesses in the data.
Generates Unique AI Trust Index
Certifai generates a numeric score based on 6 key elements of trust: fairness/bias, robustness, explainability, accuracy, compliance, and data quality. Like a credit score, the AI Trust Index enables you to compare your model to industry benchmarks and for go-live criteria within your organization.
Role-based reporting for all key stakeholders
Certifai helps you engage key stakeholders in building trusted AI with role-based reporting for:
- Data Science Teams and IT Experts
- Product and Marketing Executives
- Customers and Employees
- Compliance and Risk Executives
Make trusted AI work for you today
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