Brief Profile:

Sangeeta Singh worked as a Embedded Software Professional and Test Engineer. She is encompasses the developing simulators and automated test scripts, enhancing product quality, and supporting pilot production runs. The commitment to innovation and quality in firmware design is not just a professional mandate but also a personal passion, driving me to constantly explore new technologies that can transform the operational efficiency of embedded devices.

Abstract of the Talk:

Artificial Intelligence is reshaping products, processes, and policy—but speed without stewardship erodes trust. This invited talk translates the IEEE AI Ethics Awareness Module into a practical, engineering-first playbook for responsible AI. We'll move beyond checklists to show how transparency, accountability, privacy, fairness, robustness, and human oversight can be operationalized across the AI lifecycle—from problem framing and data design to model development, validation, deployment, and post-market monitoring. Drawing on real scenarios in embedded systems and industrial automation, I will demonstrate lightweight governance that fits fast development cycles: risk and impact screening at intake, dataset provenance and bias controls, model reporting (Cardfact sheets), safety cases and red-team tests, incident and drift response, and "human-in-the-loop" guardrails aligned with IEEE guidance. Attendees will leave with minimum-viable artefacts and decision points they can adopt immediately—whether procuring third-party models, tuning on domain data, or integrating AI into safety-critical workflows. The goal is simple: help teams ship AI that is not only innovative and compliant but measurably worthy of stakeholder trust.