Originally published July 30, 2019
Here is an excerpt:
But these algorithms can also learn the biases that already exist in data sets. If a police database shows that mainly young, black men are arrested for a certain crime, it may not be a fair reflection of the actual offender profile and instead reflect historic racism within a force. Using AI taught on this kind of data could exacerbate problems such as racism and other forms of discrimination.
"Transparency of these algorithms is also a problem," said Prof. Stahl. "These algorithms do statistical classification of data in a way that makes it almost impossible to see how exactly that happened." This raises important questions about how legal systems, for example, can remain fair and just if they start to rely upon opaque 'black box' AI algorithms to inform sentencing decisions or judgements about a person's guilt.
The next step for the project will be to look at potential interventions that can be used to address some of these issues. It will look at where guidelines can help ensure AI researchers build fairness into their algorithms, where new laws can govern their use and if a regulator can keep negative aspects of the technology in check.
But one of the problems many governments and regulators face is keeping up with the fast pace of change in new technologies like AI, according to Professor Philip Brey, who studies the philosophy of technology at the University of Twente, in the Netherlands.
"Most people today don't understand the technology because it is very complex, opaque and fast moving," he said. "For that reason it is hard to anticipate and assess the impacts on society, and to have adequate regulatory and legislative responses to that. Policy is usually significantly behind."
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