At a Glance

Name: From Data to Decisions: Explainable AI in Credit Approval
Price: Free CAD
Rating: 5.0 (1 reviews)
Author: karan_goswami, joseph_santarcangelo, kunal_makwana

Explore Explainable AI (XAI) with IBM's AI Explainability 360 (AIX 360) library to build and interpret models for credit risk assessment. This project addresses three key perspectives—data scientist, loan officer, and consumer—demonstrating how XAI enhances understanding and trust for all stakeholders. Leveraging rule-based algorithms like BooleanRuleCG (BRCG) and LogisticRuleRegression (LRR), you'll learn to develop interpretable rules that simplify applicant profile assessments. This is an ideal project for data scientists, analysts, and AI enthusiasts aiming to apply AI.

I recently delved into the field of Explainable AI (XAI) and discovered how crucial it is for enhancing transparency in high-stakes applications such as credit risk assessment. This project explores how XAI, powered by IBM's AI Explainability 360 (AIX 360) library, can transform machine learning models from black boxes into tools that not only predict but also explain their decisions, making AI-driven credit assessment more trustworthy and understandable. By using interpretable, rule-based models, you’ll gain valuable insight into the factors influencing decisions, bridging the gap between complex algorithms and human understanding.

In industries such as finance, healthcare, and law, model transparency is essential. This notebook provides an engaging experience by splitting the project into three perspectives: the data scientist, the loan officer, and the consumer. This division highlights how explainable AI serves all stakeholders, making it a powerful approach to responsible AI.

In this project, you’ll work with a real-world credit risk dataset and learn to build interpretable models using techniques such as BooleanRuleCG (BRCG), LogisticRuleRegression (LRR) and more using the AIX 360 library. You’ll preprocess the data, apply feature binarization, and use these diverse methods to generate explainable rules, prototype comparisons, and contrastive explanations.

What you'll learn

By completing this project, you will:

Preprocess and binarize features to enable interpretable modeling.
Use FeatureBinarizer to transform categorical and ordinal data.
Train interpretable models like BooleanRuleCG (BRCG) and LogisticRuleRegression (LRR).
Leverage advanced methods such as ProtoDash for finding similar profiles and contrastive explanation method (CEM) for understanding pivotal features.
Interpret model outputs from the perspectives of data scientists, loan officers, and consumers, fostering a holistic understanding of XAI in credit assessment.

Significance of this project

Explainable AI is reshaping how AI models are applied in industries by ensuring that decision-making is clear and justified. This project equips you with practical skills to create transparent models for credit risk assessment, offering:

Real-world relevance: Build models that promote transparency in financial decision-making.
Comprehensive experience: Master multiple XAI techniques, from BRCG and LRR to ProtoDash and CEM, in an end-to-end workflow.
Stakeholder insights: Learn how XAI benefits various roles, creating trust and accountability across stakeholders.

Who should enroll

This project is ideal for:

Data scientists and machine learning engineers interested in explainable AI techniques.
Financial analysts who seek to understand credit risk through interpretable models.
Tech enthusiasts who want to explore XAI applications in high-stakes fields.
Students or professionals aiming to improve transparency in AI-driven decisions.

What you'll need

Before starting this project, ensure you have:

Basic knowledge of Python and machine learning.
An interest in explainable AI techniques.
Access to a programming environment (e.g., Jupyter Notebook).
Familiarity with credit risk assessment concepts is helpful but not required.

Why enroll

By the end of this project, you’ll be able to build and interpret AI models for credit risk assessment using IBM’s AIX 360 library and a range of XAI methods. This hands-on experience will provide you with transferable skills, enabling you to bring interpretability and trust to AI models across industries. Whether you’re in finance, healthcare, or another field requiring responsible AI, this project will deepen your understanding of XAI and its potential to drive transparency in machine learning.

Offered By: IBMSkillsNetwork