Emergency responders are increasingly functioning as knowledge workers relying on complex information systems, social media, and digital communication for situational awareness, coordination, and appraisal of their distributed efforts in crisis contexts. How should AI systems and HCI approaches effectively support these workers, whether in Emergency Operations Centers or in the field? What are the implications for interpreting, trusting, and engaging with AI systems to facilitate and coordinate relief efforts in crisis?
How do we design better tools, methods, and best practices that fuse cooperative distributed knowledge among fieldworkers and autonomous systems in disaster settings? In this paper I examine prior work to illustrate the key challenges, conditions, and unexplored opportunities emerging in these distributed workplaces, that increasingly rely on real-time communication, mobile applications, and social media analytics. Designing for current and future scenarios that incorporate machine learning to better augment crisis response, presents many challenges, risks, and opportunities that must be carefully explored.
Sawhney, N., 2019. Cooperative Crisis Response among Emergency Responders & AI Systems (PDF). Position paper for Workshop on Better Supporting Workers in ML Workplaces, the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing, November 9-13, 2019, Austin, TX, USA.