@inproceedings{qian2026human,
author = {Qian, Alice and Chandhiramowuli, Srravya and Dabbish, Laura A. and Shen, Hong and Taylor, Alex S and Wang, Ding and Skeadas, Theodora and Jagdagdorj, Bolor-Erdene},
title = {Human Expertise for AI Red-Teaming and Scalable Evaluation},
booktitle = {CHI 2026},
year = {2026},
month = {April},
abstract = {Rapid adoption of generative AI has outpaced the infrastructure needed to red team systems responsibly. This workshop tackles a core tension: scaling AI red teaming while centering human expertise and well-being. We convene academic, industry, and nonprofit practitioners for two threads. (A) Vision: surface high-level goals and principles for effective, humane red teaming. (B) Build: identify opportunities to support human-AI red teaming, such as scenario libraries, role prompts for red teamers, and calibration methods that align automated efforts with human expertise. Through this workshop, we will develop a vision for the future of effective AI red teaming that leverages and protects human expertise while meeting the needs of evaluation at scale.},
url = {http://approjects.co.za/?big=en-us/research/publication/human-expertise-for-ai-red-teaming-and-scalable-evaluation/},
}