New Research Aims to Solve the Problem of AI Bias in "Black Box" Algorithms (Technology Review)

New Research Aims to Solve the Problem of AI Bias in "Black Box" Algorithms From picking stocks to examining x-rays, artificial intelligence is increasingly being used to make decisions that were formerly up to humans. But AI is only as good as the data it's trained on, and in many cases we end up baking our all-too-human biases into algorithms that have the potential to make a huge impact on people's lives. In a new paper published on the arXiv, researchers say they may have figured out a way to mitigate the problem for algorithms that are difficult for outsiders to examine--so-called "black box" systems. A particularly troubling area for bias to show up is in risk assessment modeling, which can decide, for example, a person's chances of being granted bail or approved for a loan. It is typically illegal to consider factors like race in such cases, but algorithms can learn to recognize and exploit the fact that a person's education level or home address may correlate with other demographic information, which can effectively imbue them with racial and other biases.