DEBIZ (Exam DEZ-110): Data Ethics for Business Professionals

Demystifying Data Ethics

This course dives into the critical intersection of data, technology, and ethics.  Explore the power of AI, data science, and machine learning, while understanding how ethical principles differ between human and machine decision-making.

Empower Yourself and Your Organization

Learn core concepts of data ethics and their application to emerging technologies. Discover the potential pitfalls of ignoring ethical considerations and equip yourself to make responsible decisions.

Objectives

To provide business professionals and consumers of technology core concepts of ethical principles, how they can be applied to emerging data-driven technologies and the impact to an organization that ignores the ethical use of technology.

Who Should Attend?

This course is designed for business professionals and technology users who want to understand ethical considerations in the data-driven world.

Pre-Requisite

To ensure your success in this course, you should have a working knowledge of general business concepts and practices. It would help if you also had a basic understanding of Artificial Intelligence and Data Science. You can obtain this level of skills and knowledge by taking the following CertNexus courses:

• AIBIZ Artificial Intelligence for Business Professionals
• DSBIZ Data Science for Business Professionals

Methodology:

• Interactive learning with real-world case studies.
• Hands-on exercises to solidify understanding.
• Invest in responsible technology adoption. Enroll today!

Outline

Below is the course content, which includes a detailed outline of topics and materials covered in the course. Explore and enhance your knowledge!

Introduction to data ethics

• Define ethics
• Define data
• Define data ethics
• Principles of data ethics
• The case for data ethics
• Identifying ethical issues

Ethical principles

• Ethical frameworks
• Applying ethical frameworks
• Privacy, fairness, and safety
• Applying privacy, fairness, and safety principles
• Algorithms and human-centered values
• Discussing true and false positives and negatives
• Discussing accuracy and precision
• Discussing correlation and causation
• Transparency and Explainability: the black box problem

Discussing black box parallels

• Inclusive growth, sustainable development, and well-being
• Examining a tech for good organization
• Improving Ethical Data Practices

Sources of ethical risk

• Bias and discrimination
• Case study: Allegheny family screening tool
• Data Surveillance
• Safety and Security
• Case study: PredPol
• Business considerations
• Data legislation
• Manage the effects of data
• Case study
• Embed organizational values in the data value chain
• Building a data ethics culture/code of ethics
• Stakeholder checklist