About the workshop:

The core work of a lot of current AI systems is to describe, predict or construct phenomena based on data and/or observations. However, data and the observations as well as how we evaluate the goodness-of-fit of these models is a function of our history, culture, and our position in society. In this talk, we will look at the human decision we make while building AI systems, which manifest in the technical decisions taken by these models. We will illustrate these decisions using two systems - a neighborhood analysis and classification system and a language learning system.

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.