Visible Leadership at the Edge: Building Trust & Equity in Embedded Sensor AI

Session details:

As sensor-AI systems increasingly operate at the edge—from industrial monitoring to wearables and MedTech—system reliability isn’t enough. Stakeholders, users, and regulatory entities demand visible trust, safety, and equity. In this session, Sheena Yap Chan introduces the VISIBLE Framework (Voice, Identity, Spotlight, Inner Work, Belief, Leverage, Elevation) tailored for sensor designs and embedded AI. Using real-world examples (e.g., edge deployment failures, bias in sensor data, energy constraints), attendees will explore how leaders can build visibility through clear communication, inclusive practices, and trustworthy design. Participants will leave with actionable strategies to improve stakeholder confidence, address equity in sensor deployment, and lead product innovations with integrity.

Key Learning Objectives

By the end of this session, attendees will be able to:

• Apply the VISIBLE Framework to increase transparency and trust in edge-based sensor and AI systems.
• Identify equity and bias risks in sensor data collection, model inference, and deployment contexts.
• Design communication and leadership strategies that align sensor teams and stakeholders for sustainable, ethical adoption.