#bias-in-ai

#bias-in-ai

The "AI Action Plan" prioritizes artificial intelligence innovation and adoption, urging the removal of any "red tape" that could be slowing adoption across industries and government.

Today, most AI is being built on blind faith inside of black boxes. It requires users to have on unquestioning belief in something neither transparent nor understandable.

The alarming reality is that AI-driven learning can perpetuate biases present in data sources, leading to unfair treatment and unequal opportunities for diverse learners.

AI therapy chatbots are increasingly common and can be helpful in a narrow, focused way, but human factors in therapy cannot be easily simulated.

AI models are extraordinary, but they can also fail for some groups of people: if a model is trained only on data from men, for example, it might perform poorly when looking at women.

In our experiments, we attempted the following standard datasets in the machine learning literature to assess the effectiveness of DP-based Fair Learning.