We simply can’t ignore the rapid rise of Artificial Intelligence in our lives today. Artificial Intelligence is everywhere today, whether you talk about multi-billionaire corporations or emerging start-ups. According to the CyberGhost blog post, it is reported that pretty much every company will somehow use AI for one thing or the other by 2025, making it more than valuable in the market.
Without any doubt, the use of AI might have made our lives easier, but that doesn’t mean it has no flaws. Like everything else, AI also has flaws, and they are more than just emerging from discriminatory bias. Of course, the bias and the flaws of AI don’t come up on their own.
AI simply feeds off the data it is given and reflects accordingly. The fact that there is bias and discrimination in AI interpretation means that we are feeding AI data with discriminatory factors. The biases seen in data are coming from us and the society that is training, developing and using these systems.
How AI Images Reinforce Stereotypes
There is more than one way in which AI images can reinforce stereotypes through a gallery of several interconnected mechanisms. These mechanisms reflect biases that are present in the data and the algorithms that we feed it. Here is how the AI images reinforce stereotypes-
Training Data Bias
Many AI models are heavily based on machine learning techniques, including deep learning that requires training data. If the training data set contains any bias or any societal stereotypes, the AI model will learn the same and give results in the images generated.
For example, if you are trying to generate images in leadership roles, and the data you feed mostly has men in the said roles, the images will be generated reflecting those stereotypes.
Feature Representation Bias
AI learning models also learn to represent the data through the different layers of features. These features are interpreted from the training data and can reflect both genuine and discriminatory features present in the data.
If you are feeding the data containing several dark-toned attributes or associations with criminal activities, the AI will generate images with those stereotypes.
Contextual Biases
If the training data has context that implicit biases or stereotypes/assumptions, the AI will incorporate the mentioned contexts in the results generated.
For example, if you ask the AI to generate images from different professional fields and the context has specific genders or regions mentioned, the images will be generated based on the data while reflecting the contextual biases.
Feedback
AI models also learn and unlearn through layers of feedback it is given. Its an iterative learning process, where the AI models change their outputs and adjust the behavior accordingly.
If an AI model is generating output based on stereotypical contexts and data, and the feedback is positive in that regard, the AI model will learn to prioritize that data. This feedback loop will amplify the volume of biases in the present data and train the model accordingly.
Ambiguity and Interpretation
Not all AI models generate images explicitly showing stereotypes or bias. It is also based on the user’s assumption and the interpretation of the AI images generated. Many users interpret these AI-generated images as an interpretation of the stereotypes embedded in the society.
Wrapping Up
It is important that we understand how AI reinforces stereotypes in the images generated by the training data models and how quickly we can address these biases at the various stages of the AI development process.
The various measures that we can take to remove the biases and the stereotypes could include modifying the training data, carefully selecting the examples for eliminating the biases, and involving diverse stakeholders in the design, developmental, and evaluation stages of the AI systems to help identity these biases at the earliest.
