Wat je moet weten voordat je
begint

Start 5 June 2026 19:26

Einde 5 June 2026

00 Dagen
00 Uren
00 Minuten
00 Seconden
course image

Ethics and Safety in Open AI

Discover frameworks and tools for responsible generative AI use, covering bias detection, safety guardrails, content provenance, and regulatory compliance through hands-on exercises.
Coursera via Coursera

Coursera

2874 Cursussen


Not Specified

Optionele upgrade beschikbaar

Not Specified

Ga in je eigen tempo vooruit

Paid Course

Optionele upgrade beschikbaar

Overzicht

The Ethics and Safety in Open AI course equips learners with the frameworks and tools needed to ensure responsible use of generative AI models. The course begins with bias detection and mitigation, where learners identify harmful patterns in datasets and outputs, apply quantitative evaluation techniques, and implement mitigation strategies.

Next, learners design and test safety guardrails, including input validation, output filtering, content moderation, and red-teaming practices to strengthen AI systems against misuse. The final module covers content provenance, licensing, and compliance, where learners apply watermarking techniques, implement provenance standards such as C2PA, and evaluate datasets and models for licensing adherence.

Regulatory frameworks like GDPR and CCPA are also introduced. Through hands-on exercises, learners will build safety layers, implement provenance metadata, and prepare compliance-ready audit documentation.

By the end, learners will be able to design open AI applications that prioritize safety, fairness, and accountability.

Lesprogramma

  • **Module 1: Bias Detection and Mitigation**
  • Introduction to bias in AI
    Identifying harmful patterns in datasets and outputs
    Quantitative evaluation techniques for bias detection
    Mitigation strategies for bias reduction
  • **Module 2: Designing and Testing Safety Guardrails**
  • Input validation techniques
    Output filtering methods
    Content moderation strategies
    Introduction to red-teaming practices
    Strengthening AI systems against misuse
  • **Module 3: Content Provenance, Licensing, and Compliance**
  • Watermarking techniques for content provenance
    Implementing provenance standards (e.g., C2PA)
    Evaluating datasets and models for licensing adherence
    Introduction to regulatory frameworks (e.g., GDPR, CCPA)
    Preparing compliance-ready audit documentation
  • **Practical Hands-on Exercises**
  • Building safety layers in AI systems
    Implementing and managing provenance metadata
    Preparing and conducting compliance audits
  • **Course Wrap-up**
  • Review of key concepts in AI safety and ethics
    Best practices for designing responsible open AI applications
    Ensuring fairness, accountability, and safety in AI development

Gegeven door

Professionals from the Industry


Vakgebieden

Artificial Intelligence