Ze Shen Chin and Rokas Gipiškis represented AI Standards Lab at the Second Workshop on Technical AI Governance Research, held at ICML 2026 in Seoul, South Korea, presenting three papers co-authored with external research collaborators. All three projects were mentored by Chin and Gipiškis through the Supervised Program for Alignment Research (SPAR).
1. Open Problems in AI Incident Governance
Examines existing frameworks for AI incident governance and finds inconsistencies in how incidents are defined, classified, monitored, and reported. Identifies open problems at each stage of the incident governance pipeline and proposes monitoring and reporting principles, concrete monitoring guidelines, and a reporting template.
Paper Authors: Harleen Kaur Sidhu, Rebecca Scholefield, Nour Annan, Kevin Hernandez, Isabel Nieh Hou, Abdulrahman Alshaikhi, Ze Shen Chin, Rokas Gipiškis
2. Defining AI Models and AI Systems: A Framework to Resolve the Boundary Problem
Draws on a systematic review of 896 academic papers and a manual review of regulatory documents to resolve the ambiguity around the term “AI model” in frameworks such as the EU AI Act. Proposes conceptual and operational definitions, anchoring the model in trained parameters and architecture.
Paper Authors: Yuanyuan Sun, Timothy Parker, Lara Gierschmann, Sana Shams, Teo Canmetin, Mathieu Duteil, Rokas Gipiškis, Ze Shen Chin
3. Principles and Guidelines for Randomized Controlled Trials in AI Evaluation
Establishes a framework for standardising AI evaluation RCTs (human uplift studies), extending the Shadish-Cook-Campbell four-validity framework with a fifth principle on transparency, repeatability, and verification, operationalised into 33 guidelines.
Paper Authors: Christopher Kelly, Angelica Chowdhury, Alexandra Campili, Bimpe Ayoola, Devin Barbour, Thomas Chen Dawson, Ze Shen Chin, Rokas Gipiškis