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Data Ethics for Business Professionals (DEBIZ)

Course number: CGIDEBIZ40

The power of extracting value from data utilizing Artificial Intelligence, Data Science and Machine Learning exposes the learning differences between humans and machines. Humans can apply ethical principles throughout the decision-making process to avoid discrimination, societal harm, and marginalization to maintain and even enhance acceptable norms. Machines make decisions autonomously. So how do we apply ethical principles to data driven technology? This course provides business professionals and consumers of technology with the core concepts of ethical principles, how they can be applied to emerging data driven technologies, and the impact to an organization which ignores ethical use of technology.

  • To ensure your success in this course, you should have a working knowledge of general business concepts and practices.
  • You should also have a basic understanding of Artificial Intelligence and or Data Science.
Target Audience

This course is designed for business leaders and decision makers, including C-level executives, project and product managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who have a vested interest in the representation of ethical values in technology solutions. Other individuals who want to know more about data ethics are also candidates for this course. This course is also designed to assist learners in preparing for the CertNexus® DEBIZ™ (Exam DEB-110) credential.


CertNexus® DEBIZ™


CertNexus® DEBIZ™ Exam DEB-110


Post class completion, students may appear for the CertNexus® DEBIZ™ Exam DEB-110.

Course Content
Lesson 1: Introduction to Data Ethics
  • Topic A: Define Ethics
  • Topic B: Define Data
  • Topic C: Define Data Ethics
  • Topic D: Principles of Data Ethics
  • Topic E: The Case for Data Ethics
Lesson 2: Ethical Principles
  • Topic A: Ethical Frameworks
  • Topic B: Privacy, Fairness and Safety
  • Topic C: Algorithms and Human-Centered Values
  • Topic D: Transparency and Explainability: The Black Box Problem
  • Topic E: Inclusive Growth, Sustainable Development, and Well-Being
Lesson 3: Sources of Ethical Risk
  • Topic A: Bias and Discrimination
  • Topic B: Data Surveillance
  • Topic C: Safety and Security
Lesson 4: Business Considerations
  • Topic A: Data Legislation
  • Topic B: Manage the Effects of Data
  • Topic C: Embed Organizational Values in the Data Value Chain

Available Formats

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