Susanna Raj

Cognitive Science Researcher & Founder, AI4Nomads

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Susanna Raj
Cognitive Science Researcher & Founder, AI4Nomads

Susanna Raj is an independent cognitive science & AI ethics researcher with a background in psychology, studio arts, and human-computer interactions. She is also an inclusive AI instructor at the Business School of AI, a nationally recognized public speaker, and an affective computing researcher with more than seven years of experience. Her focus areas include cognitive and social science in AI, AI ethics, inclusive AI, biometric privacy regulations, emotion and memory sciences, and social awareness of AI bias.

Susanna is the founder and CEO of AI4Nomads, a mobile data labeling platform that pays anyone to train and learn AI on a mobile device. In alignment with the United Nations Sustainable Goals for 2030, the platform harnesses the power of AI to eradicate poverty and digital illiteracy around the globe. Previously, Susanna was a researcher at the Intel Anticipatory Computing Lab and a language data collection researcher at Amazon Lab126. She is a published researcher for the Association of Computing Machinery, the Conference on Neural Information Processing Systems, and Women in Machine Learning. She is also a judge, mentor, and organizer of AI hackathons. Susanna was a 2022 nominee for the Women in AI North America Responsible AI Leader award and a recipient of the President’s Volunteer Service Award. Women in AI Ethics recognized her as one of 20 Rising Stars in AI Ethics and DataEthics4All named her as one of 100 DIET Champions, for her exemplary work in one or more fields of data and diversity, inclusion and impact, ethics and equity, in teams and technology. She holds volunteer leadership posts, including partnership lead for Women in AI North America, co-editor of Women in AI Ethics magazine, and leadership council member at DataEthics4All.

Susanna is a prolific writer and an established studio artist. She enjoys capturing oddities with her camera, believing they teach more about constructs for “normal” than anything else.