Governance & Safety
A track of P15 · Responsible AI — Ethics, Safety & Governance.
Risk tiers and obligations, LLM failure modes, safety evaluation and incident thinking - governance as a working skill, not a compliance checkbox.
Governance sounds like paperwork until the day a model in production hurts someone and everyone asks who was supposed to catch it. This track treats governance and safety as a working skill: the judgement to place a system in the right risk tier, spot how it can fail, test for those failures, and respond when one lands.
It opens with the landscape — the EU AI Act's risk tiers, accountability and human-oversight expectations — presented as a map a practitioner uses, not legal text to memorise. Then LLM-specific safety through the responsibility lens: hallucination, prompt injection and misuse categories, framed by what makes each dangerous and who owns the risk (the mechanics live in the LLM pillars; here it is the safety framing). Safety evaluation and red-teaming come next — as governance practice, with the sober counterweight that a passing eval proves far less than it appears to. The track closes on incident thinking: a real case walked end to end — detect, contain, communicate, remediate, prevent — the responsible-AI playbook for the day something goes wrong.