Abeba Birhane, Cognitive Scientist
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Abeba Birhane, Cognitive Scientist

Abeba Birhane is a cognitive scientist, currently a Senior Advisor in AI Accountability at the Mozilla Foundation and an Adjunct Assistant Professor in the School of Computer Science and Statistics at Trinity College Dublin (working with Trinity’s Complex Software Lab). She researches human behaviour, social systems, and responsible and ethical artificial intelligence and was recently appointed to the UN’s Advisory Body on AI.
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Born in Ethiopia, cognitive scientist Abeba Birhane works in the nexus of critical race studies, machine learning, complex adaptive systems, and algorithmic bias. Through his collaboration with Vinay Prabhu, Birhane discovered that unpleasant photos and racist and sexist labels were included in large-scale image datasets like ImageNet and 80 Million Tiny photos, which are frequently used to construct AI systems.

Ethiopia is where Birhane was born. The Open University awarded her a Bachelor of Arts in Philosophy and a Bachelor of Science in Psychology. She finished her Ph.D. in 2021 at the Complex Software Lab in the University College Dublin School of Computer Science after earning her Master of Science in Cognitive Science in 2015.


2023

Birhane and Vinay Prabhu, chief machine learning scientist at UnifyID, examined the issues with massive picture datasets’ labeling, categorization, and data collecting in a study released in 2020. Thousands of artificial intelligence algorithms and systems have been developed using these datasets, which include ImageNet and MIT’s 80 Million Tiny Images. Birhane and Prabhu discovered that in addition to unpleasant photos, they included several labels and obscenities that were sexist and racial. As a result, the 80 million Tiny Images collection was voluntarily and formally removed by MIT.

Recently, Birhane has collaborated on studies on the obstacles to data sharing in Africa alongside Rediet Abebe, George Obaido, and Sekou Remy. Even when the data originates from Africa, they discovered that power disparities play a big role in the data sharing process. At the ACM Conference on Fairness, Accountability, and Transparency, their study was published.


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