An Asian MIT student asked AI to turn an image of her into a professional headshot. It made her white with lighter skin and blue eyes.::Rona Wang, a 24-year-old MIT student, was experimenting with the AI image creator Playground AI to create a professional LinkedIn photo.
They should just call AIs “confirmation bias amplifiers”.
AI learns what is in the data.
The AI we have isn’t “learning”. They are pre-trained.
The “pre-training” is learning, they are often then fine-tuned with additional training (that’s the training that isn’t the ‘pre-training’), i.e. more learning, to achieve specific results.
Stereotype machines
Humans will identify sterotypes in AI generated materials that match the dataset.
Assume the dataset will grow and eventually mimic reality.
How will the law handle discrimination based on data supported sterotypes?
How would that happen, exactly?
Stereotypes themselves and historical bias can bias data. And AI trained on biased data will just learn those biases.
For example, in surveys, white people and black people self-report similar levels of drug use. However, for a number of reasons, poor black drug users are caught at a much higher rate than rich white drug users. If you train a model on arrest data, it’ll learn that rich white people don’t use drugs much but poor black people do tons of drugs. But that simply isn’t true.
That’s just stupid and shows a lack of understanding of how this all works.