Discrimination by algorithm: scientists devise test to detect AI bias

 at the Guardian: “There was the voice recognition software that struggled to understand women, the crime prediction algorithm that targeted black neighbourhoods and the online ad platform which was more likely to show men highly paid executive jobs.

Concerns have been growing about AI’s so-called “white guy problem” and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making.

Mortiz Hardt, a senior research scientist at Google and a co-author of the paper, said: “Decisions based on machine learning can be both incredibly useful and have a profound impact on our lives … Despite the need, a vetted methodology in machine learning for preventing this kind of discrimination based on sensitive attributes has been lacking.”

The paper was one of several on detecting discrimination by algorithms to be presented at the Neural Information Processing Systems (NIPS) conference in Barcelona this month, indicating a growing recognition of the problem.

Nathan Srebro, a computer scientist at the Toyota Technological Institute at Chicago and co-author, said: “We are trying to enforce that you will not have inappropriate bias in the statistical prediction.”

The test is aimed at machine learning programs, which learn to make predictions about the future by crunching through vast quantities of existing data. Since the decision-making criteria are essentially learnt by the computer, rather than being pre-programmed by humans, the exact logic behind decisions is often opaque, even to the scientists who wrote the software….“Our criteria does not look at the innards of the learning algorithm,” said Srebro. “It just looks at the predictions it makes.”

Their approach, called Equality of Opportunity in Supervised Learning, works on the basic principle that when an algorithm makes a decision about an individual – be it to show them an online ad or award them parole – the decision should not reveal anything about the individual’s race or gender beyond what might be gleaned from the data itself.

For instance, if men were on average twice as likely to default on bank loans than women, and if you knew that a particular individual in a dataset had defaulted on a loan, you could reasonably conclude they were more likely (but not certain) to be male.

However, if an algorithm calculated that the most profitable strategy for a lender was to reject all loan applications from men and accept all female applications, the decision would precisely confirm a person’s gender.

“This can be interpreted as inappropriate discrimination,” said Srebro….(More)”.