Shafee Mohammed
ORCID:
0000-0003-4231-9960
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About
Contact & Profiles
A5032002477
Research Areas
- Intelligent Tutoring Systems and Adaptive Learning
- Cognitive Abilities and Testing
- Human-Automation Interaction and Safety
- Cognitive Functions and Memory
- Cognitive Science and Mapping
- Adversarial Robustness in Machine Learning
- Educational Games and Gamification
- Online Learning and Analytics
University of California, Irvine
2017
10.1007/s41465-017-0047-y
article
EN
Journal of Cognitive Enhancement
2017-11-28
Bertie Vidgen
Adarsh Agrawal
Ahmed Mohamed Ahmed
Victor Akinwande
Namir Al-Nuaimi
and 92
more
Najla Alfaraj
Elie Alhajjar
Lora Aroyo
Trupti Bavalatti
Borhane Blili-Hamelin
Kurt Bollacker
Rishi Bomassani
Marisa Ferrara Boston
Siméon Campos
Kal Chakra
Canyu Chen
Cody Coleman
Zacharie Delpierre Coudert
Leon Derczynski
Debojyoti Dutta
Ian Eisenberg
James Ezick
Heather Frase
Brian Fuller
Ram Gandikota
Agasthya Gangavarapu
Ananya Gangavarapu
James Gealy
Rajat Ghosh
James Goel
Usman Gohar
Sujata Goswami
Scott A. Hale
Wiebke Toussaint Hutiri
Joseph Marvin Imperial
Surgan Jandial
Nick Judd
Felix Juefei-Xu
Foutse Khomh
Bhavya Kailkhura
Hannah Rose Kirk
Kevin Klyman
Chris Knotz
Michael Kuchnik
Shachi H Kumar
Chris Lengerich
Bo Li
Zeyi Liao
Eileen Peters Long
Victor M. Lu
Yifan Mai
Priyanka Mary Mammen
Kelvin Manyeki
Sean McGregor
Virendra Mehta
Shafee Mohammed
Emanuel Moss
Lama Nachman
Dinesh Jinenhally Naganna
Amin Nikanjam
Besmira Nushi
Luis Oala
Iftach Orr
Alicia Parrish
Cigdem Patlak
William Pietri
Forough Poursabzi-Sangdeh
E. Presani
Fabrizio Puletti
Paul Röttger
Saurav Sahay
Tim Santos
Nino Scherrer
Alice Schoenauer Sebag
Patrick Schramowski
Abolfazl Shahbazi
Vin Sharma
Xudong Shen
Vamsi Sistla
Leonard Tang
Davide Testuggine
Vithursan Thangarasa
Elizabeth Anne Watkins
Rebecca Weiss
Chris Welty
Tyler Wilbers
Adina Williams
Carole-Jean Wu
Poonam Yadav
Xianjun Yang
Yi Zeng
Wenhui Zhang
Fedor Zhdanov
Jiacheng Zhu
Percy Liang
Peter Mattson
Joaquin Vanschoren
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by MLCommons Working Group. The Benchmark designed to assess safety risks systems that use chat-tuned language models. We introduce a principled approach specifying and constructing benchmark, for covers only single case (an adult chatting general-purpose assistant in English), limited set personas (i.e., typical users, malicious vulnerable users). new taxonomy 13 hazard categories, 7 have tests benchmark. plan...
10.48550/arxiv.2404.12241
preprint
EN
arXiv (Cornell University)
2024-04-18
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