The abrupt and opaque firing of AI bias researcher Timnit Gebru last month generated enormous amounts of press and attention. The reverberations were felt at the top AI research conference, NeurIPS, where a schism is emerging in the field of AI research because of the heavy influence that the major tech vendors like Google, Microsoft, and Amazon hold, including comparisons between Big Tech and Big Tobacco's tactics at a Resistance AI workshop.
This survey revealed a "wild west" culture in which well-known and documented biases inherent in large language models are ignored as a result of breakneck development. It highlights the broader need for a more comprehensive approach to documentation and data management. We concur, while thinking the problem of bias in ML extends well beyond the data. We see those same discussions happening in the private sector as technologists and business owners grapple with the potential harm that intelligent systems can inflict on companies' bottom lines, risk profiles, and public brand.