Developers of artificial intelligence must learn to collaborate with social scientists and the people affected by its applications.
Beginning in 2013, the Dutch government used an algorithm to wreak havoc in the lives of 25,000 parents. The software was meant to predict which people were most likely to commit childcare-benefit fraud, but the government did not wait for proof before penalizing families and demanding that they pay back years of allowances. Families were flagged on the basis of ‘risk factors’ such as having a low income or dual nationality.
But these won’t be enough to make AI equitable. There must be practical know-how on how to build AI so that it does not exacerbate social inequality. In my view, that means setting out clear ways for social scientists, affected communities and developers to work together. To spur fruitful collaborations, mandates and approaches need to be designed more effectively. Here are three principles that technologists, social scientists and affected communities can apply together to yield AI applications that are less likely to warp society.Vague calls for broader participation in AI systems miss the point. Nearly everyone interacting online — using Zoom or clicking reCAPTCHA boxes — is feeding into AI training data.
By contrast, social-work researcher Desmond Patton at Columbia University in New York City has built a machine-learning algorithm to help identify Twitter posts related to gang violence that relies on the expertise of Black people who have experience with gangs in Chicago, Illinois. These experts review and correct notes underlying the algorithm. Patton calls his approach Contextual Analysis of Social Media .
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