I&T Solution

ITSolutionRef

S-0546

Solution Name

Using Explainable AI for transparent and reliable ML models

Solution Description

As AI grows at a rapid pace, it is vital to keep up with digital transformation in traditional industries, especially governance. However, the complexity of the novel solutions makes technology incompliant and difficult to interpret. At Glassbox AI, we aim to shed light on these black box models with the use of our proprietary Explainable AI (XAI), providing the ability to understand how predictions are made. Backed by researchers and professors, our XAI allows for overall explanations of how a model behaves, break down individual predictions into its features, model performance evaluations as well as fairness and bias calculations.

By adopting XAI, companies can improve the transparency, accountability, compliance, user understanding, and error detection capabilities of Artificial Intelligence (AI)systems. Overall, the use of our XAI services would lead to more effective and equitable decision-making processes.


Application Areas

Broadcasting

City Management

Climate and Weather

Commerce and Industry

Development

Education

Employment and Labour

Environment

Finance

Food

Health

Housing

Infrastructure

Law and Security

Population

Recreation and Culture

Social Welfare

Transport

Technologies Used

Artificial Intelligence (AI)

Data Analytics

Deep Learning

Machine Learning

Predictive Analytics

Use Case

1. Banking and Finance: Enhance customer trust and mitigate risk of discrimination while conducting credit scoring, fraud detection, and investment management using transparent and fair Artificial Intelligence systems.

2. Regulatory Services: Increase accountability of trained models dealing with sensitive data by ensuring decisions are made fairly and without bias.

3. Healthcare: Ensure interpretability and accuracy in medical diagnosis, treatment recommendations, drug development and more with ExplainableAI.


Our XAI allows projects to leverage cutting edge technologies responsibly due to model explainability and ethical AI standards to ensure fairness. This makes machine learning adoption trustworthy for all stakeholders, including institutions running these projects as well as the general public.

If any government department would like to conduct PoC trial or technology testing on the I&T solution, please contact Smart LAB.