Hybrid Intelligence models perform at or above best-in-class traditional AI method metrics on predictive performance.
- No trade-off between explainability and performance
- High performance models that are transparent and robust
- Best in class in fit-for-purpose decisions
Hybrid Intelligence models have a known and observable behaviour which means that they can be treated like any expert system, they can be:
- Audited with certainty
- Tested and certified for compliance to requirements and regulations
- Applied to critical applications that may require the inclusion of fallbacks, guard rails and other safety measures
Transparent models and precise prediction explanations mean:
- Faster Insights: deeper understanding of the underlying data patterns, correlations, and causal relationships.
- Trust and Transparency: confidence in system reliability and fairness to users, stakeholders, and regulators.
- Compliance and Accountability: satisfying legal and ethical requirements in regulated industries by providing auditable and justifiable explanations.
The absolute certainty and real-time availability of query and model explanations enable:
- Identification, Assessment, and Resolution any non-compliant, unaligned or sub-optimal characteristics of a prediction before it is consumed as a decision
- Build better decisions by using explanation data to improve predictions in real-time workflows
- Selectively de-automate using explanations to effectively include humans-in-the-loop (HITL) where needed.
- Train directly from with in-built, fully integrated data pre-processing
- Less data: better generalisation reduces the quantity and quality of training data needed
- Leverage existing knowledge from legacy systems to enhance predictive model training
- Train once: save time and resources with a training algorithm that trains the optimal model in one training run.
- Model behaviour is precisely known and explanations are guaranteed to be precise and correct
- Real-time model activation mapping tools give detailed and live monitoring data for anomaly detection, retraining scheduling, cyberthreat detection
- Simple and reliable integration into systems using symbolic interfaces