. Researchers say that in many cases, training an algorithm on algorithmically generated data increases the risk that an artificial intelligence system will perpetuate harmful discrimination.
One of the most common ways to create synthetic data is with a generative adversarial network, or GAN, a methodwhereby two neural networks are pitted against each other. First, both are trained on similar sets of real data. Then the first network, or generative model, attempts to synthesize data realistic enough that it will fool the second network, the discriminatory model, into believing the synthesized data came from the same source as the real training data.
after discovering it favored men over women due to the historical employment data it was trained on—but GAN-generated synthetic data can amplify the bias.. They started with a data set composed of 17,245 images of engineering professors from universities across the country, 80 percent of whom were male and 76 percent of whom were white. They then trained a GAN on that data set to create synthetic images.
Julia Stoyanovich, a computer science professor at New York University, says the debate in the industry shouldn’t be “accuracy versus fairness.” That is, companies don’t have to choose. Instead, “the data should represent the world how it should be.”
It is not racism nor sexism if certain people cannot achieve the same things as others.