Evaluating and Monitoring Data Bias in GenAI Models

Detect and correct unfair outcomes from biased training data.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course focuses on identifying, assessing, and mitigating bias in Generative AI models to ensure fair and ethical AI outcomes. Participants will learn techniques to detect bias in training data and model outputs, understand its impacts, and implement strategies for continuous monitoring and correction.

Learning Objectives

Participants will be able to:

• Recognize different types of data and algorithmic bias

• Use tools and methods to detect bias in GenAI models

• Analyze the sources and consequences of bias in training datasets

• Apply mitigation techniques to reduce bias and improve fairness

• Establish monitoring frameworks for ongoing bias detection

Target Audience

• Data scientists and machine learning engineers

• AI ethics and compliance officers

• Product managers and AI project leads

• Business stakeholders focused on responsible AI

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive sessions with real-world bias detection tools

• Case studies illustrating bias impact and mitigation

• Hands-on labs for bias evaluation and correction

• Group discussions on ethical AI practices

Materials Provided

• Bias detection software and scripts

• Sample datasets with bias scenarios

• Guidelines for bias mitigation strategies

• Reading materials on AI fairness and ethics

Outcomes

• Ability to identify and quantify bias in GenAI systems

• Skills to implement bias mitigation techniques effectively

• Framework for continuous monitoring of AI fairness

• Enhanced awareness of ethical responsibilities in AI development

Outline / Content

Day 1: Understanding Bias in GenAI

• Types of bias: data, algorithmic, and societal

• Impact of bias on AI fairness and decision-making

Day 2: Detecting Bias in Training Data and Models

• Tools and techniques for bias detection

• Hands-on exercises with biased datasets

Day 3: Mitigating Bias in Generative AI

• Methods for bias reduction and fairness enhancement

• Case studies of successful bias mitigation

Day 4: Monitoring and Governance for Fair AI

• Setting up bias monitoring frameworks

• Policies and ethical guidelines for ongoing fairness