AI algorithms have become foundational in operational activities for many organizations. The broadened scope of AI and ML algorithms and their usage allows businesses to unlock operational efficiencies, strategy planning, and new boundaries for customer experience. While there is so much about using these algorithms, ineffective management can lead to operational, financial, and regulatory risks.
Customer interactions with chatbots, fraud detection, or patient symptom checker with AI still involve human supervision to resolve possible biases and refine the algorithm further. Organizations need AI Assurance services beyond simple algorithmic control assessments.
Superior AI Performance – Roadblocks
Human supervision is still crucial in assessing the readiness for deploying AI systems.
Significant time and effort in establishing deployment-ready models.
Lack of trust and questions about performance for Unsupervised AI models.
Expensive data science talent required to maintain Supervised AI models.
AI models age faster than traditional software. Concept drift makes data models obsolete.
Our AI Assurance Services
We at Saxon apply a holistic assurance approach to assess the elements in AI algorithms’ lifecycle, validating the complex AI ecosystem. How do we address this? Our experts will initiate with the algorithm validation and enhance trust with our custom AI Assurance framework.
Algorithm design assessment
Will the current AI system design match according to the output from the system?
Data quality, preparation, and collection methodology
Assessing the confidence in the data quality and integration methods used for training the AI models.
Modeling methods assessment
Defining and articulating the output of each AI system.
Performance metrics assessment
Interpreting the performance metrics to the AI algorithms and design output.
Operational and deployment assessment
Interaction of AI systems with the market consistently, planned vs. deviations.
AI Assurance Framework
As we continue to leverage AI for internal business operations, the quality of AI systems will be a key differentiator in building a data-driven enterprise. Our experts at Saxon can help you with the AI assurance framework alongside algorithm validation for consistent performance and adaption to dynamic scenarios.
Pre-deployment AI Assurance
- Ensure optimal training and testing before the AI model moves to production.
- Choice of data set to match the production AI system closely.
- Data bias – Eliminate any data bias by checking the test results and failure patterns
- Result evaluation and establishing failure protocols
- Data sanity and privacy assessment
Post-deployment AI Assurance
- User action analysis – continuous feedback monitoring to analyze success rates
- Establish model failure thresholds according to the AI application
- AI performance monitoring to identify root cause for performance deviation
- Based on business scenarios, identify any new data sources or parameters
- Iterate testing during pre-deployment based on any changes
