Artificial Intelligence and Biased Behaviors
Recently, a friend of mine sent me a video 🎥. In the video, two people were complaining about not being able to get an AI tool to draw a "full wine glass 🍷." My friend asked me, "Is this true?" 🤔. Curious, I decided to try it myself, and unfortunately, the results weren’t much different than I expected 😅.
When you ask an AI model to "draw a wine glass filled to the brim," it generally can’t do it correctly 🚫. It keeps drawing a glass half full. I tested this issue on different platforms like Gemini, ChatGPT, and Copilot, but the results were nearly the same 🧐.
So, why is this happening?
AI models are heavily influenced by the datasets they are trained on. The model learns from the examples it encounters most frequently in the training data, which can sometimes lead to biased behavior 🧠. A "full wine glass" might be a rare example in the training data 🤨. Therefore, the model tends to repeat the "half full" glasses it has seen more often.
Another example came up when I tried to create a professor avatar without a beard, mustache, or glasses 👨🏫. Similarly, the model kept drawing professors with beards, mustaches, and glasses 🖼️. This bias in the AI's drawings is actually a result of the distribution in the training data. It tends to repeat items it encounters with high probability and ignores those that are rarely seen 📊.
⚖️ Bias, in fact, refers to the tendency of the model to repeat examples it has seen with high probability in the statistical distribution of the training data ⚖️. This leads to some elements, especially those encountered rarely, being overlooked 🧐. In other words, the AI's ability to generate accurate or creative solutions is limited by the data it has been exposed to 🚧.
Such biases serve as an important reminder for AI developers and users 📢: The diversity and balance of training data play a crucial role in achieving more accurate and inclusive outcomes 🌍. It is clear that more effort is needed to make AI more objective and unbiased 👩💻👨💻.
#AI #ArtificialIntelligence #Bias #MachineLearning #Technology #DataScience #AIethics #Innovation #MuratKarakayaAcademy
