Objectives:

Upon completion of this course, participants will be able to:

• Analyze how **algorithmic bias** is introduced and perpetuated in credit scoring models (data, feature selection, model training).
• Differentiate between the legal requirements of **Fair Lending/Equal Access** and the technical challenge of AI fairness.
• Apply methodologies for **bias detection and measurement** in automated credit decisions (e.g., statistical parity, equal opportunity).
• Understand the role of **Explainable AI (XAI)** and interpretability in meeting regulatory non-discrimination requirements.
• Develop and implement strategies for **bias mitigation and remediation** in machine learning models used for lending.
• Evaluate the specific regulatory risks associated with using **non-traditional data** (e.g., social media, geospatial) in credit scoring.
• Design a robust **Model Governance Framework** that integrates fairness and compliance checks for ADM.
• Understand the regulatory and ethical mandate for transparency and consumer "right to explanation" for credit denials.

• Data Scientists and Machine Learning Engineers in Financial Institutions
• Compliance Officers and Fair Lending Specialists
• Internal Auditors and Model Risk Management Professionals
• Regulators overseeing Fair Lending and Technology Risk
• FinTech Executives and Product Development Leads
• Legal Counsel specializing in AI/ML and Discrimination Law
• Public Policy Makers focused on Ethical AI and Digital Inclusion

• Case Studies analyzing technical reports on algorithmic bias in lending platforms.
• Group Activities in applying fairness metrics to a simulated model output data set.
• Discussions on the trade-offs between accuracy and fairness in model selection.
• Individual Exercises on drafting a compliant Adverse Action Notice based on XAI output.
• Workshop on developing a structured audit protocol for an AI credit model.
• Expert demonstration of popular bias detection and interpretability toolkits.

Unit 1: Foundations of Algorithmic Bias and Fairness

• Overview of Automated Decision-Making (ADM) in credit underwriting and pricing.
• How bias enters the system: Historical data, proxy variables, and feature engineering.
• Legal and ethical concepts of **algorithmic fairness** (e.g., group fairness vs. individual fairness).
• Case studies of known algorithmic discrimination in financial services.

• Applying **Fair Lending and Equal Access** laws to algorithmic credit decisions.
• Interpreting **Disparate Impact** and **Disparate Treatment** in a Machine Learning context.
• Regulatory guidance and principles for the ethical use of AI in finance.
• The challenge of regulating "black box" models and ensuring auditability.

Unit 2: Bias Detection and Measurement Techniques

• Defining and calculating key **fairness metrics** (e.g., Demographic Parity, Equalized Odds).
• Statistical methods for identifying proxy variables for prohibited bases.
• Techniques for analyzing model outputs and residuals for evidence of disparity.
• Establishing a threshold for an **acceptable level of disparity** in regulatory compliance.

• Auditing training data for historical bias and underrepresentation.
• Identifying and treating **sensitive features** and their indirect correlation with outcomes.
• The role of non-traditional data (e.g., utility payments, browsing history) in mitigating or exacerbating bias.
• Pre-processing techniques for de-biasing input data and feature sets.

Unit 3: Mitigation, Remediation, and Explainability (XAI)

• In-processing techniques: Constrained optimization and adversarial de-biasing.
• Post-processing techniques: Score calibration and threshold adjustment.
• Designing a **remediation plan** for models found to have material bias.
• Ethical considerations when modifying models for fairness goals.

• The regulatory need for **model transparency** and the consumer's "right to explanation."
• Techniques for local and global interpretability (e.g., LIME, SHAP values).
• Translating complex model explanations into clear, compliant **Adverse Action Notices**.
• Regulatory requirements for internal model documentation and validation.

Unit 4: Model Governance and Oversight

• Developing a dedicated **AI/ML Model Governance Policy** with fairness built-in.
• Establishing a **Model Risk Management (MRM)** function focused on bias.
• The role of the compliance function and internal audit in model validation.
• Regulatory guidance on ongoing monitoring and re-validation for model drift.