Meredith Whittaker

“the types of stories we tell about intelligence, about computational sophistication, were serving to entice people to trust and lend credibility to ultimately what are large companies, large corporate actors who may or may not have as their core objective broad social benefit and who create these technologies and these tools behind shrouds of corporate secrecy.”

“I became very concerned initially that the data sources that were being used and the claims that were being made about these models created with this data were not accurate, were not sufficient to reflect the things that were being claimed, were certainly not sufficient to create an intelligent machine”

“I knew that data was always imperfect. It was always partial. It was never the whole story. And so in the early twenty tens, when I saw first machine learning and then we all decided to use the term AI become the next big things following the Alex Net paper, following the recognition that GPUs could effectively supercharge the compute available for neural network training.”

“And so I was effectively at the heart of the processes for creating data and then making claims about that data as a reliable representation of dynamic reality. And I knew how difficult that was.”

“I think I was initially interested and maybe a bit concerned around AI based not on my literature, my rhetoric backgrounds, but because I had done a lot of work building large scale measurement systems.”