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CertNexus

Certified Ethical Emerging Technologist™ (CEET)

Demonstrate your mastery of ethical integrity in emerging technology – AI, IoT and data science – in 3 instructor-led days. This hands-on course teaches you to apply foundational ethical principles, industry-standard frameworks and risk-management techniques to design and govern responsible, transparent and fair data-driven solutions – preparing you for the CertNexus® Certified Ethical Emerging Technologist™ (Exam CET-110) credential.

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Why choose this course?

  • Vendor-neutral, cross-industry focus. You’ll learn ethical best practices that apply equally to AI, IoT, data science and beyond.
  • Frameworks & hands-on labs. Apply the IEEE, EU AI Act and NIST ethics frameworks in guided scenarios to identify and mitigate real-world risks.
  • Governance & policy design. Build the skills to draft organizational policies, codes of ethics and access-control processes that ensure accountability.
  • Transparency & fairness tooling. Explore practical methods for explainability, bias detection and privacy-by-design in modern data pipelines.
See Course Content

This course is ideal for:

  • Tech leaders, architects and managers who guide AI, IoT or data-science initiatives.
  • Compliance, risk and ethics officers charged with safeguarding new technology deployments.
  • Developers and engineers who embed ethical checks into code, models and device firmware.
  • Anyone preparing for Exam CET-110 on their CertNexus ethical-technology certification journey.

Prerequisites

  • General familiarity with data-driven technologies (AI, IoT or data science).
  • Foundational understanding of organizational governance or risk-management concepts.

Course Content

  • Foundations of Ethical Emerging Technology – core concepts in AI, IoT and data science; history and importance of ethics in tech; overview of Zero Trust and privacy-by-design principles.
  • Ethical Frameworks & Standards – compare IEEE, EU AI Act, NIST and ISO/IEC guidelines; translate framework requirements into actionable design controls.
  • Risk Identification & Analysis – spot ethical risks across data collection, model training, sensor deployment and user consent; map risks to stakeholders and data flows.
  • Risk Mitigation Strategies – apply bias-detection methods, privacy-enhancing technologies (PETs) and secure-by-design patterns for algorithms and devices.
  • Organizational Governance & Policy Design – draft codes of ethics, data-usage policies and access-control processes; implement entitlement management and audit mechanisms.
  • Transparency, Explainability & Accountability – implement model-explanation tools, logging/audit trails and user-facing disclosures for AI and IoT systems.
  • Fairness, Non-Discrimination & Inclusion – evaluate algorithms for demographic parity and disparate impact; design remediation workflows for biased outcomes.
  • Building an Ethical Culture – foster ethical leadership, training programs and continuous-improvement cycles; integrate ethics reviews into SDLC and DevOps pipelines.

Hardware Requirements

Interested?

Enquire today and one of our consultants will be in touch.