Responsible Data Usage and AI Ethics

Mitigate bias, respect privacy, and ensure fair data practices.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course explores the ethical considerations and responsible practices for using data in Generative AI projects. Participants will learn how to identify and mitigate bias, protect privacy, and uphold fairness in data collection, processing, and application to ensure trustworthy AI outcomes.

Learning Objectives

Participants will be able to:

• Understand key ethical principles in data usage for AI

• Recognize and reduce biases in datasets and AI models

• Implement privacy-preserving techniques and data protection measures

• Apply frameworks for fairness, accountability, and transparency

• Develop policies for responsible data governance in AI projects

Target Audience

• Data scientists and AI practitioners

• Data governance and compliance officers

• Business leaders overseeing AI initiatives

• Legal and ethics professionals

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures on AI ethics and responsible data practices

• Case studies highlighting bias and privacy challenges

• Group discussions and role-playing on ethical dilemmas

• Practical exercises in bias detection and mitigation

Materials Provided

• Ethical guidelines and frameworks for AI data use

• Tools for bias detection and privacy enhancement

• Case study summaries and reference documents

• Templates for responsible data governance policies

Outcomes

• Enhanced awareness of ethical issues in AI data usage

• Skills to identify and address bias and fairness challenges

• Ability to implement privacy and security best practices

• Capacity to lead responsible data governance initiatives

Outline / Content

Day 1: Foundations of AI Ethics and Responsible Data Use

• Introduction to ethics in AI and data

• Principles of fairness, accountability, and transparency

Day 2: Bias in Data and AI Systems

• Types and sources of bias

• Techniques for bias detection and mitigation

Day 3: Privacy and Data Protection

• Data privacy laws and regulations

• Methods for privacy-preserving data handling

Day 4: Implementing Responsible Data Governance

• Policies and frameworks for ethical data use

• Case studies and best practices in AI ethics