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“What concerns me most is the idea that we’re coming up with systems that are supposed to ameliorate problems [but] that might end up exacerbating them,” says Kate Crawford, co-founder of the AI Now Institute, a research centre at New York University that studies the social implications of artificial intelligence.
With Crawford and others waving red flags, governments are trying to make software more accountable. Last December, the New York City Council passed a bill to set up a task force that will recommend how to publicly share information about algorithms and investigate them for bias. This year, France’s president, Emmanuel Macron, has said that the country will make all algorithms used by its government open. And in guidance issued this month, the UK government called for those working with data in the public sector to be transparent and accountable. Europe’s General Data Protection Regulation (GDPR), which came into force at the end of May, is also expected to promote algorithmic accountability.
In the midst of such activity, scientists are confronting complex questions about what it means to make an algorithm fair. Researchers such as Vaithianathan, who work with public agencies to try to build responsible and effective software, must grapple with how automated tools might introduce bias or entrench existing inequity — especially if they are being inserted into an already discriminatory social system.
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