RADAR: Robust AI-Text Detection via Adversarial Learning
Xiaomeng Hu, Pin-Yu Chen, et al.
NeurIPS 2023
Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today—with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care—requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.
Xiaomeng Hu, Pin-Yu Chen, et al.
NeurIPS 2023
Adriana Alvarado Garcia, Marisol Wong-Villacres, et al.
CHI 2023
Miriam Rateike, Brian Mboya, et al.
DLI 2025
Anshita Verma, Vagner Figueredo de Santana, et al.
AGU Fall 2022