Recognizing & Managing Bias in AI
Optional Deep-Dive Sessions
Once the core foundation is in place, the chapter can offer any combination of the following deep-dive sessions based on member interest. Each stands on its own and can be scheduled independently.
Session 6 — Recognizing & Managing Bias in AI
Optional Deep-dive· 1 hour
Bias is one of the most consequential — and most misunderstood — risks in any AI initiative. It rarely arrives as a single dramatic failure; far more often it enters quietly through the data, the design choices, or the way results are interpreted, and surfaces only after a solution is in production. For the project professional, bias is not solely a data-science concern; it is a governance, risk, and stakeholder-trust issue that belongs squarely in the project plan.
This session demystifies where bias comes from across the AI lifecycle, how to recognize the warning signs, and what practical controls a PM can build into scoping, data review, validation, and human oversight. We connect bias management to the HITL workstream and to responsible-AI governance, so attendees leave able to treat fairness as a measurable, manageable project objective rather than an afterthought.
Participants will be able to:
- Describe the common sources of bias across the AI lifecycle — data, design, and interpretation.
- Recognize the warning signs of bias and define practical checkpoints and metrics to surface it early.
- Build bias and fairness controls into project governance, validation, and human-in-the-loop oversight.
About the Series Cutting Through the Noise: An AI Learning Series for Project Professionals
Artificial intelligence is moving from headline to job requirement. Project managers are increasingly being asked to scope, sponsor, govern, and deliver initiatives that include an AI component — often before they have had a chance to build a grounded understanding of what these initiatives actually involve. The result is a familiar pattern: high expectations, a great deal of noise, and projects that stall because the conversation never moved past the hype.
This proposal outlines a progressive learning series designed specifically for the project professional. Rather than treating AI as a technology lecture, the series builds awareness, then literacy, then practical competence — always anchored in the project management fundamentals and PMI frameworks the chapter’s members already trust. The goal is not to turn PMs into data scientists. It is to help them recognize an AI opportunity, ask the right questions, align stakeholders, and lead the delivery of AI capabilities that produce measurable organizational value.
The series is structured as three core sessions that establish a shared foundation, a set of optional deep-dive sessions the chapter can select from based on member interest, and a capstone full-day workshop. Sessions are modular: the chapter can run the full program or select the pieces that best fit its calendar and audience.
Speakers
