Don't Attack the Artist, Fix the Palette! And Give it a Break!

A seemingly inevitable concern troubled the AI image generation landscape. Google's new AI chatbot Gemini, otherwise used worldwide for its interactive, iterative, and problem-solving capabilities, faced worldwide criticism for generating images that perpetuated harmful stereotypes, often representing historical figures or events with a clear bias towards white individuals.  This happened during February 2024 and understandably sparked a heated debate about the ethics and potential dangers of AI-generated visuals. There were strong remarks of Google's apathy and turning a blind's eye towards ethical AI in competition with Microsoft Copilot's Image Generation tool, which is already famous with its DALL:E capabilities.

How This Works

For most of us (like me, who only understand AI from managing a program or a project, not technically unless one of my Data Scientist takes all the pain to explain me!), let's imagine that Gemini (or for that matter DALL:E) AI image generator as a student with a massive library like the great Library of Congress in the US. at its fingertip and the student has a superpower to go through many books at a single time. This bright young student learns by understanding and internalizing countless images and the descriptions that accompany them. So, if the books mainly feature a certain type of person or depict events in a biased way, the AI will, unfortunately, internalize those same biases.

The Actual Problem

It's tempting and easy to blame the AI itself, to call it insensitive or malicious. Some even tried to misuse the tool to satisfy their perverse mindsets, manipulating prompts to bypass safeguards. But it's important to remember that AI is not an evil overlord. It's a tool that mirrors the data it learns from. Think of our library-loving student again. If they've only been exposed to books that present a skewed worldview, they can't magically produce unbiased images. Similarly, the AI can only reflect the data it's been fed. The issue lies in the inherent biases present within those massive datasets of images and text descriptions.

Who fed these images and how biases creep in?

Understanding where the AI's images come from is key to unravelling why biases exist in the first place. Here's a breakdown of the culprits:

1. Dataset Creators: Companies and researchers compile massive image databases, and their choices on what to include or exclude can inadvertently introduce bias.
2. Data Sources: Much data comes from the web, which reflects existing societal prejudices. Think of historical images, websites, or social media, where stereotypes and underrepresentation persist.
3. Unconscious Bias: Even the most well-intentioned developers may unintentionally miss subtle bias patterns, leading to skewed datasets despite their best efforts. Examples of Common Biases are:

• Racial/Ethnic Bias: Overrepresentation of white individuals, underrepresentation of people of colour. Gender Bias: Stereotyping roles (e.g., women primarily in domestic settings, men in positions of power).
• Cultural Bias: Focus on Western history and events, lack of diverse cultural representation.