Ethical AI: A Course on Data Ethics & Privacy in Analytics

Introduction

In an era where data is a powerful asset, the ethical implications of its use have become a critical concern for every organization. Our course, Ethical AI: A Course on Data Ethics & Privacy in Analytics, is designed to equip data professionals and business leaders with the knowledge and tools to navigate the complex landscape of data ethics, privacy, and responsible AI. You will learn to move beyond technical capabilities to build systems that are not only powerful and effective but also fair, transparent, and respectful of individual rights.

This immersive, five-day program will provide a comprehensive framework for understanding and implementing ethical data practices. We will delve into key topics such as algorithmic bias, data security, and compliance with global privacy regulations. Through practical case studies and hands-on exercises, you will learn how to identify ethical risks, develop a strong governance framework, and foster a culture of responsible innovation within your organization. This course is an indispensable guide for anyone committed to building a more trustworthy and equitable data ecosystem.

Duration

5 days

Target Audience

This course is for data scientists, analysts, machine learning engineers, data managers, compliance officers, and business leaders who are involved in the collection, use, and analysis of data. A basic understanding of data concepts is helpful, but no prior expertise in ethics or law is required.

Course Objectives

  1. Understand the fundamental principles of data ethics and their real-world impact.
  2. Identify and mitigate different types of algorithmic bias in machine learning models.
  3. Master key privacy-enhancing technologies for data protection.
  4. Navigate the legal and regulatory landscape of data privacy (e.g., GDPR, CCPA).
  5. Develop a framework for ethical decision-making in data projects.
  6. Apply techniques for ensuring fairness and transparency in AI systems.
  7. Explore the challenges of data ownership, consent, and accountability.
  8. Create a data governance plan that incorporates ethical guidelines.
  9. Analyze and critique real-world case studies of ethical failures in data.
  10. Build a proactive approach to responsible innovation and data stewardship.

Course Modules

Module 1: Foundations of Data Ethics

  • What is data ethics?
  • The moral and ethical principles of data collection and use.
  • Key concepts: privacy, fairness, transparency, and accountability.
  • The societal impact of data-driven decisions.
  • Case study: real-world examples of ethical dilemmas in data.

Module 2: Privacy and Anonymization

  • The importance of data privacy.
  • Techniques for anonymizing data (e.g., k-anonymity, l-diversity).
  • Differential privacy and its applications.
  • Challenges of re-identification in anonymized datasets.
  • Hands-on lab: a basic data anonymization exercise.

Module 3: Algorithmic Bias

  • What is algorithmic bias?
  • Types of bias: selection bias, confirmation bias, and historical bias.
  • Identifying bias in data and models.
  • Methods for fairness auditing.
  • Hands-on lab: detecting bias in a sample dataset.

Module 4: Fairness in AI

  • Defining fairness in machine learning.
  • Fair machine learning metrics (e.g., disparate impact, equal opportunity).
  • Techniques for mitigating bias (e.g., pre-processing, in-processing, post-processing).
  • The fairness-accuracy trade-off.
  • Practical project: building a fair classification model.

Module 5: Transparency & Explainability

  • The "black box" problem in machine learning.
  • The need for model interpretability.
  • Techniques for explaining model decisions (e.g., LIME, SHAP).
  • Building transparent and explainable AI systems.
  • Case study: explaining a credit score decision to a user.

Module 6: Data Governance and Policy

  • The role of data governance in ethical practice.
  • Developing a data ethics policy for an organization.
  • Data lineage and accountability.
  • Creating a data ethics committee.
  • Practical project: drafting a data ethics charter.

Module 7: Data Privacy Laws and Regulations

  • A deep dive into global privacy regulations.
  • The General Data Protection Regulation (GDPR).
  • The California Consumer Privacy Act (CCPA).
  • Understanding data subject rights (e.g., right to be forgotten).
  • Compliance requirements for data professionals.

Module 8: The Ethics of Personal Data

  • The concept of data ownership and digital identity.
  • The importance of informed consent.
  • Data-driven surveillance and its ethical challenges.
  • The ethics of social media data.
  • Discussion on the future of personal data.

Module 9: Ethical Data Collection

  • Best practices for collecting data ethically.
  • The importance of transparency in data collection.
  • The ethics of scraping and using public data.
  • The role of data collection in reducing bias.
  • Case study: ethical data collection for a social good project.

Module 10: Ethical AI in Practice

  • Case studies in different industries (healthcare, finance, law).
  • The ethics of autonomous systems and self-driving cars.
  • AI in hiring and talent management.
  • Ethical challenges of generative AI and large language models.
  • Group discussion: ethical dilemmas in a specific industry.

Module 11: Security and Data Protection

  • The role of security in data ethics.
  • Protecting sensitive data from breaches.
  • The principles of data minimization and purpose limitation.
  • Secure data storage and transmission.
  • Hands-on lab: a brief introduction to secure coding practices.

Module 12: Building a Responsible AI Culture

  • Fostering an ethical mindset in your team.
  • The importance of diverse teams in AI development.
  • Creating a culture of accountability.
  • The role of leadership in promoting data ethics.
  • Project: a plan for an ethical AI workshop.

Module 13: Auditing for Ethics

  • The concept of an ethical audit.
  • Tools and frameworks for ethical auditing.
  • The role of external auditors.
  • Establishing a reporting and remediation process.
  • Hands-on lab: an ethical audit of a sample AI system.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Ethical Ai: A Course On Data Ethics & Privacy In Analytics in Guinea-Bissau
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