About the workshop:

The core work of machine learning is to describe and/or predict phenomena based on data or observations from that phenomena and others related to it. However, data and the observations we make as well as how we evaluate for correctness about a phenomena are a function of history, culture and our position in society. In this talk we will look at the process of machine learning model development and the diversity of decisions taken by developers which manifest in the technical decisions taken by these models. We do this by focusing on the tensions which occur when developers/engineers change course based on where they believe the true solution is situated versus where the algorithm will go without their guidance. We wish to especially emphasize that these decisions are not inconsequential to the final functioning of the algorithms and therefore it is also not inconsequential who builds machine learning models.

About the speaker:

Nyalleng Moorosi is a senior researcher at Distributed Artificial Intelligence Research (DAIR) institute. Her interests are in understanding how we can build models which center populations often regarded as peripheral. Before DAIR she was a research software engineer at Google, a senior researcher at the South African Council for Scientific and Industrial research, where she worked closely with government and academic institutions to develop a diversity of products for private and public institutions. Outside of formal work she is involved in efforts to democratize AI and is a co-founder of the Deep Learning Indaba, the largest machine learning consortium of AI/ML practitioners in Africa.

RSVP for meeting link: https://ccny.zoom.us/meeting/register/tZUkc-msrzgrGNJoq4t9c5HjFhkMnMfvG…

This event is sponsored by Standard Chartered Bank's Futuremakers Initiatives.