Introducing TrustyCore
Companies must implement AI/ML into existing and new processes to remain competitive in the market. AI/ML can automate decisions, identify hidden properties, and predict events. However, there are dangers if AI is affected by bias, by manipulation, or by hallucinations. Companies must be able to support government regulations, keep themselves out of court, and defend themselves in case of litigation. TrustyCore enables companies to “explain” why their systems made the decisions they did and add humans
into the loop.
What is AI Explainability?
Explainable AI (XAI) refers to methods and techniques in artificial intelligence that offer insights into the decision-making processes of machine learning models. The goal is to produce models that are transparent and interpretable, enabling human users to understand, trust, and effectively manage AI-driven outcomes.
TrustyCore adds to this by allowing business users, data scientists, or developers to introspect decisions and understand the “why” of the decision. This can reduce risk, help to defend decisions, and ensure support of government regulations.
Human In the Loop
Add human review to AI/ML decision processes, override high risk decisions based on governance, and feed changes back into your model
Explainability
Understand the reasoning for AI/ML decisions, what factors drove the reasoning, discover counter-factual data to support a different outcome
Governance
Ensure support for government regulations, corporate policy, industry liabilities, enforce risk management requirements and policy reviews
Leverage TrustyCore for Explainabile AI
An open community providing a transparent product for transparent AI/ML
TrustyCore provides enterprise support for the TrustyCore Build of TrustyAI, and TrustyCore Services to provide seamless integration with existing AI/ML applications with a focus on transparency, empowering companies to deliver responsible and reliable AI/ML solutions.
