NEW! Navigating AI Ethics: A Guide for Project Managers with Carlene Szostak
What You Will Learn
Upon completion of this training, learners will be able to:
- Identify and address ethical concerns using established AI frameworks.
- Apply practical tools to manipulate and manage ethical risks in AI-driven projects.
- Differentiate global AI regulations and their impact on project management.
- Create strategies to promote transparency, fairness and accountability in AI systems.
Description
This training provides project managers with essential ethical guidelines and practical skills to responsibly implement and oversee artificial intelligence (AI) technologies. Through case studies, discussions and hands-on exercises, participants will explore real-world scenarios addressing the moral and legal implications of AI in project management and develop strategies for making ethical decisions in AI projects. Additionally, attendees will gain insights into the latest AI trends and best practices, equipping them to stay ahead in a rapidly evolving technological landscape.
OUTLINE
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- Welcome and Introduction
- Foundations of AI Ethics
- History of AI
- Ethical considerations over time
- Code of Ethics and its relevance to AI
- The role of bias and fairness in AI decision-making
Activity: Group discussion on how ethical theories apply to AI in project management.
- Legal and Regulatory Considerations in AI
- Global AI regulations and standards
- Data privacy, protection and compliance
- Ethical risks in project management: accountability, transparency and consent
Activity: Case study: Analyze a real-world AI project that failed due to regulatory and ethical concerns.
- Transparency, Accountability and Explainability in AI
- Black-box algorithms and ethical concerns
- Strategies for making AI systems more transparent and accountable
- Role of the project manager in ensuring ethical AI systems
Activity: Role-playing scenario where participants must justify decisions made by an AI system to stakeholders.
- Bias, Fairness and Inclusivity in AI
- Sources of bias in AI algorithms
- Techniques to mitigate bias
- Fairness and inclusivity in AI projects
Activity: Interactive exercise: Review a biased AI algorithm and work in teams to identify the ethical issues and propose solutions.
- AI in Decision-Making: Human Oversight
- Project managers in AI-driven decision-making
- Challenges of human–AI collaboration
- Ethical guidelines for the human oversight of AI
Activity: Role-play a scenario where human oversight was critical in AI decision-making and the ethical implications
- Ethics in AI-Driven Project Management Tools
- Current AI software
- How to incorporate ethics into AI project planning, development and deployment
- Monitoring for ethical compliance — how often?
- PMI's role in upholding ethical standards in AI projects
Activity: Breakout groups develop an ethical checklist for each phase of the AI project life cycle.
- Final Reflection and Action Plan
Activity: Participants individually draft an action plan outlining how they will apply ethical AI principles in their project management practices.
- Q&A and Wrap-Up
PDU Allocation Table
Ways of Working | Power Skills | Business Acumen | Total | |
---|---|---|---|---|
CAPM® / PMI-CP™ / PMP® / PgMP® | 3.5 | 0 | 3.5 | 7.00 |
PMI-ACP® / Agile* | 0 | 0 | 3.5 | 3.50 |
PMI-SP® | 0 | 0 | 3.5 | 3.50 |
PMI-RMP® | 0 | 0 | 3.5 | 3.50 |
PfMP® | 0 | 0 | 3.5 | 3.50 |
PMI-PBA® | 0 | 0 | 3.5 | 3.50 |