AI and Data Ethics in Projects
Are you ready for innovation and would you like to take on social responsibility at the same time? Then you might want to deal with ethical dilemmas and ways to measure the quality of artificial intelligence. If you would like to learn more about the obligations related to AI applications and actively participate in current discussions, then look forward to this training. Together we analyze case studies, discuss the global effects of AI development and intensively address the requirements of the EU AI Act and its consequences. In addition, you will learn the special features of project management of AI projects and simulate a moot court.
Certificate of attendance from Spirit in Projects Foundation
Goals
- Understanding AI ethics and applying them in practice
- Develop ethical guidelines for the use of AI in the company
- Address privacy requirements and bias challenges
- Discuss and implement relevant legal framework conditions
- Analyze best practices for ethical AI development
- Successfully plan and manage AI projects
- Implement MLOps and model governance
Target Groups
Content
1. Basics
- Ethics and AI
- Principles of ethical AI development
- AI ethics dilemmas in practice
- Ethical reason
- Responsible AI
2. Data protection
- Overview of the GDPR
- Principles of data processing
- Rights and obligations
- Role of the data protection officer
- Challenges with AI systems
- Privacy by design
- Anonymization and pseudonymization
3. EU AI Act
- Overview of the EU AI Act
- Classifying AI systems: risk categories
- Prohibited AI practices
- High-risk AI systems
- Responsibilities and duties
- Transparency and documentation requirements
- Consequences for non-compliance
- Practical implementation in the company
4. Bias and fairness
- What is bias in AI systems?
- Types of Bias: Data Bias, Algorithmic Bias, Human Bias
- Impact of unfair AI systems
- Fairness metrics and definitions
- Bias detection methods
- Mitigation strategies
- Fairness-aware machine learning
- Case studies on bias incidents
5. Quality in AI systems
- Quality in AI: What does that mean?
- Dimensions of quality
- Metrics for different AI systems
- Independent testing procedures
- Validation and verification
- Quality assurance challenges
- Recommendations and trends
- Continuous evaluation
6. An international perspective
- EU: AI Act and digital strategy
- USA: AI Executive Order and regulatory approaches
- China: Social Scoring and AI Regulation
- Qatar: AI strategy and development
- United Arab Emirates: AI Innovation and Governance
- Global standards and harmonization
- Comparison of regulatory approaches
7. Copyright and Legal Challenges
- Copyright Overview
- Exceptions for AI training
- Copyright and digital media
- AI-generated content: who owns it?
- Current Court Cases and Precedents
- Liability issues with AI systems
- Contract drafting for AI projects
8. Project management of AI projects (NEW)
- AI project lifecycle: From idea to deployment
- Special features of AI projects vs. traditional IT projects
- Iterative development and experimentation
- Proof of Concept (PoC) design and evaluation
- Data Science Workflow: CRISP-DM for AI projects
9. Roles and team organization (NEW)
- Roles in AI teams: Data Scientists, ML Engineers, AI Product Managers
- Responsibilities and interfaces
- Interdisciplinary collaboration
- Skill requirements and continuing education
- External vs. internal expertise
- Stakeholder management in AI projects
- Budget, resources and KPIs
10. MLOps and Model Governance (NEW)
- What is MLOps?DevOps for Machine Learning
- CI/CD pipelines for ML models
- Model versioning and experiment tracking
- Model registry and deployment
- Monitoring and retraining
- A/B testing and model evaluation in production
- Tools: MLflow, Weights & Biases, Kubeflow
11. Moot Court
- Simulation of a court case on the AI ethics of a project
- Role distribution and preparation
- Implementation and argumentation
- Reflection and lessons learned
Certification
For this training you will receive a certificate of participation from Spirit in Projects.
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