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Communities Must Develop More Inclusive AI to Avert Bias
By Tina Nord  |  Sep 15, 2021
Communities Must Develop More Inclusive AI to Avert Bias
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The human factor behind algorithmic bias is currently the subject of heated debate among scientists, politicians, and journalists. The goal of Cambridge, Massachusetts-based the Algorithmic Justice League (AJL) is to raise awareness about the impacts of AI, equip advocates with empirical research, and galvanize researchers, policy makers, and industry practitioners to mitigate AI harms and biases.

BERLIN - Deep learning and algorithms are the foundation of decisions that affect individual fates or entire groups. Intelligent assistants calculate the suitability of applicants, analyze the most efficient route or obstacles for self-driving cars, and identify cancer on X-rays. Data is the blood in the veins of such machines: It is the basis for self-learning systems and the ultimate template for all subsequent calculations and recommendations.

This fact becomes a challenge with the advance of machine learning and artificial intelligence (AI). Because data is generated and processed by humans they cannot be perfect. Data sets reflect our own biases and pervasive prejudices. If an intelligent system works on the basis of such a data set, the result is often discrimination.

Almost all large tech companies that work with AI have already encountered the problem. In October 2018, Amazon hit the headlines as a sexist AI recruiting tool showing bias while sorting applications that contained the words ‘women’ or ‘women’s college,’ while in 2015, a Google algorithm identified people with dark skin as ‘gorillas.’

Human Factor - Cause and Solution

Organizations such as the AJL and AI Now are actively committed to combating algorithmic bias. The first proposed solution is a call to diversify the sector, which to this day mainly employs white, male professionals.

“The [AJL] mission is to raise awareness about the impacts of AI, equip advocates with empirical research, build the voice and choice of the most impacted communities, and galvanize r

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