Article by Mona Sloane: “…From New York City to California and the European Union, many artificial intelligence (AI) regulations are in the works. The intent is to promote equity, accountability and transparency, and to avoid tragedies similar to the Dutch childcare-benefits scandal.
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.
Right now, developers who design AI work in different realms from the social scientists who can anticipate what might go wrong. As a sociologist focusing on inequality and technology, I rarely get to have a productive conversation with a technologist, or with my fellow social scientists, that moves beyond flagging problems. When I look through conference proceedings, I see the same: very few projects integrate social needs with engineering innovation.
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.
Include lived experience. 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. The goal should be to get input from the most relevant participants.
Otherwise, we risk participation-washing: superficial engagement that perpetuates inequality and exclusion. One example is the EU AI Alliance: an online forum, open to anyone, designed to provide democratic feedback to the European Commission’s appointed expert group on AI. When I joined in 2018, it was an unmoderated echo chamber of mostly men exchanging opinions, not representative of the population of the EU, the AI industry or relevant experts…(More)”