Originally posted 7 July 20
Here is an excerpt:
Researchers in AI overwhelmingly focus on providing highly accurate information to decision makers. Remarkably little research focuses on serving data subjects. What’s needed are ways for these people to investigate AI, to contest it, to influence it or to even dismantle it. For example, the advocacy group Our Data Bodies is putting forward ways to protect personal data when interacting with US fair-housing and child-protection services. Such work gets little attention. Meanwhile, mainstream research is creating systems that are extraordinarily expensive to train, further empowering already powerful institutions, from Amazon, Google and Facebook to domestic surveillance and military programmes.
Many researchers have trouble seeing their intellectual work with AI as furthering inequity. Researchers such as me spend our days working on what are, to us, mathematically beautiful and useful systems, and hearing of AI success stories, such as winning Go championships or showing promise in detecting cancer. It is our responsibility to recognize our skewed perspective and listen to those impacted by AI.
Through the lens of power, it’s possible to see why accurate, generalizable and efficient AI systems are not good for everyone. In the hands of exploitative companies or oppressive law enforcement, a more accurate facial recognition system is harmful. Organizations have responded with pledges to design ‘fair’ and ‘transparent’ systems, but fair and transparent according to whom? These systems sometimes mitigate harm, but are controlled by powerful institutions with their own agendas. At best, they are unreliable; at worst, they masquerade as ‘ethics-washing’ technologies that still perpetuate inequity.
Already, some researchers are exposing hidden limitations and failures of systems. They braid their research findings with advocacy for AI regulation. Their work includes critiquing inadequate technological ‘fixes’. Other researchers are explaining to the public how natural resources, data and human labour are extracted to create AI.
The info is here.