Safe Use of AI In Automated Decision Making
The safe use of AI in decision making is vital for smarter and more efficient decision making. However, it faces two big challenges:
- AI models can sometimes make very plausible mistakes (hallucinations), take dangerous actions or (in the case of Machine Learning models) make wildly erroneous predictions.
- AI and ML models are opaque and often cannot provide a reliable rationale for their output.
Decision modeling can address both of these issues by
- Providing a framework that permits any AI generated result that contributes to automated decision making to be sanity checked and potentially ignored, modified or submitted to human review if it appears to be unusual or to contradict other sources to the decision.
- Boosting the reliability of AI models using such techniques as 'caging' (inhibiting wildly excessive results) and 'ensembling' (using multiple different AI models to vote, removing the dependency on any single model).
- Using decision models to support rationale generation and capture.
RapidGen is proud to support the
Decision Automation Organization
The RapidGen Genius Suite delivers a complete solution to develop, test, understand and deploy automated Decision Intelligence.
Determine What Decisions Need to be Made
Identify opportunities for intelligent decision making in your business guided by business criticality, complexity and need for agility.
Model your Intelligent Decisions Using a Global Standard Notation
Use DMN to define the logic and rationale of your Intelligent Decisions and their interfaces to AI agents and human-in-the-loop. Benefit from an easy to understand, no-code depiction of your business logic which scales as your business grows. Make required changes quickly and confidently.
Automatically Generate Rigorous Test Data for your Agent Based Decisions
Use RapidGen's Test Data Generator (TDG) to automatically create test data for your models. Define the test data coverage, statistical distributions and internal constraints between input fields. Generate as much data as you need and then submit it to your Intelligent Decisions. Curate test data with known expected results.
Execute your Decisions within a Secure, Performant Engine
Use the RapidGen DAE to validate and compile your decisions and check them against best practices. Then allow DAE to execute your decisions using curated or generated test data.
Visualize The Behaviour of Your Intelligent Decisions
Use RapidGen's Tableau integration to visualize the behaviour of your Intelligent Decisions across millions of data points. Gain a better understanding of the behaviour of your decisions across the gamut of your business operations and ensure your AI agents behave correctly throughout.
Recent
3 June, 2024
Level Up: How to Scale Business Rules to the Enterprise
Level Up: How to Scale Business Rules to the Enterprise Enterprise users of business rules, or even a BRMS, should be aware of the severe limitations of rules when scaling up systems. In contrast, the decision model approach improves the enterprise scalability of business rules by providing a high-level business organization of logic missing in … Continued
6 February, 2023
Measuring Increasing Business Benefits During Decision Modelling
A simple means of determining your actual headway in creating a decision model is to measure the number of test cases it can satisfy that bolster business benefit. But what test cases do you need, and how can you generate them efficiently?
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“Nothing to Declare but My Genius”: Type Safety in Decision Automation
Oscar Wilde’s retort to customs officials may be apocryphal, but oddly it has implications for decision automation. For example, is it always necessary for decisions to declare (in their design) the data type of their results? Or are there advantages to withholding this information until they are used? Should tools verify that decisions comply with … Continued