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Addressing Bias

In the image output below, AI was asked to generate images of a ”Confident Fast Food Worker” and a “Confident Accountant”. The fast food workers presented are a mix of genders and colors, while the accountants are all white-appearing men (with facial hair for some reason). Clearly biased and perpetuating stereotypes, with whole groups being omitted. Where is the equity? Where are older people? Where are the female accountants, or those of color, or just clean shaven? These are some of the critical questions to consider when reviewing AI and LLM inputs and outputs.

In the image output below, AI was asked to generate images of a ”Confident Fast Food Worker” and a “Confident Accountant”. The fast food workers presented are a mix of genders and colors, while the accountants are all white-appearing men (with facial hair for some reason). Clearly biased and perpetuating stereotypes, with whole groups being omitted. Where is the equity? Where are older people? Where are the female accountants, or those of color, or just clean shaven? These are some of the critical questions to consider when reviewing AI and LLM inputs and outputs.

Addressing Bias in Artificial Intelligence (AI) - A Practical Guide

Artificial Intelligence (AI) and Large Language Models (LLMs) systems learn from human-generated content, which means they can perpetuate stereotypes, exclude perspectives, or favor certain groups over others.

Guest Author

Jan 63 min read

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