AI Decision Coordination sample assets and notebooks.
Introduction
Key differentiators
Examples
Collecting a dataset
Training a model
Using the solution
Contact
AI Decision Coordination software analyses your data and calculates the success of tasks that are completed by automated AI, human resources, or augmentation that combines the two.
Our solution helps in obtaining the most optimal human-to-AI workload distribution and calculating the return on investment given specific business guidelines.
AI Decision Coordinator empowers business professionals to gauge the efficency of decision operators by allowing them to define and compute business metrics for various decision-making methods, whether they involve AI models, human input, or a hybrid approach, all in the context of specific datasets.
Rather than merely stating a operator's technical accuracy, it provides insights into the financial implications of correct and incorrect decisions, offering a consolidated view of business performance.
The comparison takes into account not only decision performance but also the associated costs. Users can assess how much they can save by automating their decision processes using machine learning compared to their current manual procedures.
AI Decision Coordinator allows the formulation of rules for selecting the most suitable decision operator for any future scenario, with the ultimate goal of maximizing overall business performance. This might entail assigning complex decisions to humans and reserving straightforward ones for machine learning models to optimize aggregate performance.
It enables users to compare the overall business performance of different decision operators, whether human-based, automated, or a combination of both, in order to choose the best allocation that complements the targeted process and satisfies the present business needs.
Based on the provided dataset, assess the areas where AI and humans demonstrate their optimal performance, considering the default gain/costs model.
Reevaluate the same dataset, this time accounting for the scenario-specific gain/cost model, to determine the areas where AI and humans perform best.
By utilizing the recommended distribution, we can compute the projected enhancements that AI would deliver:
To implement the solution, we begin by gathering the data. The dataset should encompass attributes relevant to the decision-making process, including the target attribute (groundTruth) generated by experts, as well as the decisions made by human resources (hClass) in the current workflow.
Sample dataset is available in the data folder: credit_with_human.csv
On top of the above, we need to also collect the response of the Machine Learning model.
With the provided dataset, we can train a model to predict the target attribute, which in our case is the risk (Risk/No Risk) associated with granting a loan. To gather additional information, we need to capture two properties: mlClass (the model's prediction) and mlConfidence (the probability associated with the prediction).
If you already have a model, please proceed to the Bring Your Own Model flow.
If not, let's explore how IBM's AutoAI can assist us in this task.
Running locally
Experiment with AIDC functionallity locally
Integrations with Cloud Pak for Data/IBM AI Governance
Integrate with several of IBM products to create end-to-end solution to govern your models.
Running on IBM Cloud
Run the solution on IBM Cloud.
Please contact us at: AIDC.Contact@ibm.com